1. science
2. mathematics
3. statistics

ISI-RSC 2014 ABSTRACT BOOK
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ISI-RSC 2014 ABSTRACT BOOK
CONTENTS
Invited Sessions
IS02
IS03
IS04
IS05
IS06
IS07
IS09
IS11
IS13
Techniques and Applications of Visualization for Exploratory Data Analysis
A Dynamic Graphical Software Called TongGrami for 4-9th Students
Examples of Matrix Visualization for Exploratory Data Analysis (EDA)
p. 11
p. 11
Computational Statistics
Integrating Sampling Weights with Cognitive Diagnosis Models
Nonparametric Bootstrap Inference in a Multivariate Spatial-Temporal Model: A
Simulation Study
p. 12
p. 12
Statistics in Industry: Quality and Manufacturing
Statistics in Pharmaceutical Manufacturing...Its Business Value and Challenges
A Negative Binomial Hurdle Model with Semi-nonparametric Random Effects for
the Study of Copper Hillocks Growth in Semiconductor Manufacturing
p. 13
p. 13
Multilevel Analysis of Social Science Data
The Impact of Social Capital on Individual Income in Malaysia: A Multilevel
Modeling Approach
A Total-Education-Year Approach to Measure Human Capital -- Revision Results
Based on the 2010 Population Census in China
Securities Statistics Compilation and Stock Market Analysis
An Empirical Study for Testing the Stock Market Efficiency and Identifying
Abnormal Return Opportunities
An Overview of the Current Status of the Securities Statistics in Turkey
Addressing Challenges in Compiling Balance of Payments Statistics
Addressing Challenges in Compiling Balance of Payments Statistics
Measuring Services Trade in Malaysia: Meeting Statistical Challenges
Statistics in Machine Learning and Applications
Reduced Kernel Sliced Inverse Regression
Bayesian Variable Selection for Finite Mixture Model of Linear Regressions
Dimension Reduction and Visualization of Time Dependent Interval Data Using
Sliced Inverse Regression
Statistical Methods in Industry: Reliability
Warranty Cost Analysis: Geometric Operational and Repair Times
Accelerated Degradation Analysis for the Quality of a System Based on the
Gamma Process
Parametric Inference for Multiple Repairable Systems under Dependent
Competing Risks
Measuring Government and Public Sector Debt
The Interest Rate Effects of Government Debt Maturity
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ISI-RSC 2014 ABSTRACT BOOK
IS14
IS15
IS16
IS17
IS18
IS19
IS20
IS21
IS22
Input Output Tables and Linkages to Economic Globalisation
Input Output Tables and Tourism Satellite Accounts: Case Study of India
Development of Inter-Country Input-Output Model: Trade in Value-Added and
Other Globalization Analysis
Regional Input Output Table: Inter-national Dependency in GMS Economy Based
on bilateral IOT in the Case of Thailand and Vietnam 2000
Application of Statistics in Islamic Banking and Finance
Identifying the Determinants of the Shariah Non-Compliance Risk via Principal
Axis Factoring and Structural Equation Modelling with Proposed Accounting
Technique as a Risk Measurement
Does Profit Sharing Provide Market Discipline in Islamic Banking System?
Statistical Inference With Dependent Data
Linear Mixed Models Using Longitudinal Linked Data
Approximation of Bayesian Predictive P-Values with Regression ABC
Econometric and Statistical Methods for Economic and Business Decision-Making
Fractional Multi-response Logit Regression Estimation of SMEs’ Financing
Decisions
To Ask or Not To Ask, That Is the Question
Pricing Private and Public Goods and Services through Valuation Methodologies
and Econometrics
Stochastic Modelling in Business and Industry: ISBIS Session
Component-based Predictive Path Modeling by means of Projections to Latent
Structures: Model Assessment and Interpretation Features with Business-related
Applications
A Bayesian Hierarchical Model for Negative Binomial Convolutions in Cluster
Process Models for Terrorist Activity
On Bayesian Estimation of Thermal Diffusivity in Materials
Measuring Macro-Financial Risks in the Post-GFC Environment
Global Interconnectedness in the Financial System
Compilation and Usage of Flow of Funds Statistics
Enhancement and Expansion of Japan’s Flow of Funds Accounts in Response to
International Recommendations After the Financial Crisis
Development of Financial Sectoral Accounts: Progress and Challenges
Micro-Data for Efficient and Comprehensive Statistical System
Using Micro-data to Support Analysis and Policy Decision at the Bank: My World
View
The Information Model at Banco de Portugal – Using Microdata to Face Central
Banks’ Challenges
Enhancing The Malaysian Tax System
The Relationship Between Tax Rates and Tax Revenue in Malaysia: Is There A
Laffer Curve?
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ISI-RSC 2014 ABSTRACT BOOK
Estimating the Size of the Hidden Economy in Malaysia?
IS23
IS24
Statistical Methods in Life Sciences
Estimating the Number of Neurons in Multi-Neuronal Spike Trains
Association of Cardiovascular Responses With Fine Particle Air Pollutions in Beijing
Dynamic Treatment Regimes for Chronic Health Conditions: An Overview of
Statistical Problems and Available Solutions
Distributions, Modeling and Inference
Statistical Reliability Analysis of a New Mixed Generalized Gamma Distribution
Mixture Poisson Point Process: Assessing Heterogeneity in EMA Analysis
On the Two-Sample Behrens-Fisher Problem with Fewer Observations than the
Dimension
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IS25
Statistics in Industry: Recent Developments in Statistical Process Control Methods
The Variable Parameters X Chart With Estimated Process Parameters
p. 48
Control Charts for Measurements With Known Or Estimated Periodicity
p. 49
A Phase-II Nonparametric CUSUM Chart for Joint Monitoring of Location and Scale p. 49
IS26
Interlinked Economies in Practice
The Mediating Effect of International Financial Linkages on the Comparison of
Optimal Monetary Policy in the Generalized Taylor and Multiple Calvo Economies
South East Asian Financial Linkages: Insights from a Global VAR
Explaining Business Cycles: News Versus Data Revision
IP27
IS28
IS30
IS31
IS32
IS33
Compilation of Economics Indicators Through Survey
FDI Survey On Special Purpose Entities (SPEs) in Luxembourg: The Case for
Monthly Granular Data
Measuring Uncertainty in the Financial Sector
p. 50
p. 50
p. 51
p. 52
p. 52
The Challenges of Using Secondary Data to Examine Levels and Distribution of
Health in Malaysia
Measuring Universal Health Coverage in Malaysia
p. 53
High-Dimensional Penalized Regression and Covariance Estimation
Regression Shrinkage and Grouping of Highly Correlated Predictors With HORSES
p. 54
Statistical Methods and Applications in Health Services and Outcome Research
Logic Regression for Provider Effects on Kidney Cancer Treatment Delivery
Evaluation of the Accuracy of A Biomarker Adjusting for Covariate Effects for
Outcome Classification
p. 55
p. 55
Analysis of Survey Data
On Invariant Post-Randomization for Statistical Disclosure Control
Identification Problem for the Analysis of Binary Data with Non-Ignorable Drop
Out
p. 57
p. 57
Socio-Economic Indices in Country Society
Where Are Peninsular Malaysia’s Most Deprived Areas?
Socio-Economic Development and Current Status On Quality of Life in Korea
p. 59
p. 60
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ISI-RSC 2014 ABSTRACT BOOK
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IS35
IS36
IS37
IS38
IS39
IS41
IS43
IS44
Application of Statistical Techniques for Surveillance and Decision/Policy Making
Is Consumer Confidence Index Useful in Forecasting Household Consumption in
Nigeria? Evidence from Survey Data
The Use of Data Reduction Techniques to Assess Systemic Risk: An Application to
the Chilean Banking System
Application of Statistics in Credit Risk Management
Why Banks Fail: A Forward Intensity Model for Default and Distressed Exits
An Application of Techniques for Improving Model Rank Accuracy Under Various
Data Conditions
Prediction of The Insolvent Borrowers in Egypt
Statistics in Action: Teaching, Research, and Consulting
Practical Experiences for Modeling Work As Statistical Collaborators and
Consultants
Education for A Workplace Statistician
Negotiating the Path of Academia: Challenges for Statisticians in Developing
Countries
Non-Parametric and Semi-Parametric Inference
In-Sample Density Forecasting
Nonparametric Analysis of Covariance in Partial Linear Models With Factor-ByCurve Interactions
Semiparametric Isotonic Regression
Statistical Methods in Public Health I
Estimation of Antibody Concentration By Weighted Average of Multiple Dilution
Data
Adjusting Reporting Bias in Passive Pharmacovigilance
Sorting Multiple Classes in Multi-Dimensional Roc Analysis: Parametric and
Nonparametric Approaches
Classification and Clustering With Applications
Quadratic Discriminant Classifier for Moment-Based Face Recognition
Global Metric Learning for Nearest Neighbor Classification
Pattern Classification for Earth Surface Temperature Changes in the Arctic
Housing Bubble: Measurement of Housing Price
Issues Related to House Price Statistics – Indian Experience
Liquidity Shocks and the US Housing Crisis of 2007-08
Monitoring House Prices from a Financial Stability Perspective – the BIS
Experience
Financial Inclusion Measurement
Financial Capability of Malaysians: Evidence From OECD (INFE) 2010 Data
International Trade and Finance
Analyzing Price Discovery Function of Crude Palm Oil Futures (FCPO) Before and
After Shari’ah Compliant
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ISI-RSC 2014 ABSTRACT BOOK
Trade Competitiveness Determinants in Emerging and Developed Countries
The Impact of Non-Tariff Barriers On The Import of Agricultural Products in
Malaysia
IS45
IS46
IS47
IS48
IS49
IS50
IS51
IS52
Extreme Value Theory (page number change from this page onwards)
Probabilistic Analysis of Extreme Rainfall in Malaysia
Extreme Value Modelling Using Hourly Rainfall Series in Klang Valley
Modelling and Analysis With Applications – I
The Fuzzified Employability Model for The Perceived Multiple Intelligence of
People With Epilepsy
Statistical Analysis of Discrete Data With COM- Poisson and Other Families of
Distributions
Estimation and Forecasting With Logarithmic Autoregressive Conditional Duration
Models: A Comparative Study With An Application
Statistical Methods in Public Health II
Estimation of Vaccination Coverage Rates By Combining Time Series and Cross
Sectional Data
A Bayesian Analysis for Detecting the Waning of Vaccine Effectiveness
Determinants of the Socioeconomics and Spatial Pattern of Malnutrition in India:
A Geoaddative Semi-Parametric Regression Approach
Modelling Risk: Theory and Applications
Trade Models With Risk Variables: A Review of Econometric Issues and Future
Directions
Evaluating the Soundness of Malaysian Commercial Banks
Well-Being and Quality of Life for Social Progress: Measures, Indicators and
Determinants
What Do Malaysians Care in the Pursuit of Happiness: A Cross-Sectional Ordered
Logistic/Probit Model Using World Value Survey
Macro Determinants of Happiness: A Panel Data Analysis
Building An Economic Indicator to Measure The Well-Being in Egypt
Statistical Business Register Enhancing Economic Efficiency
The Statistics New Zealand Business Register
Capturing the E-Commerce Data: A Brief Study and Implementation of Business
Register in Indonesia
Understanding the Macroeconomics Issues and Policy
An Empirical Study of The Transmission Mechanism and Interest Rates PassThrough: Conventional Vs Islamic
Examining the Exchange Rate Regime-Monetary Policy Autonomy Nexus: Evidence
From Malaysia
The Transmission of Financial Stress and Monetary Policy Responses in the Asean5 Economies
Robust Statistics
Comparing PRM, PLS, PCR RR Techniques to Handle Multicollinearity and Outliers
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ISI-RSC 2014 ABSTRACT BOOK
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Modelling and Analysis With Applications – II
Goodness of Fit Test for Semiparametric Logistic Regression for Correlated Binary
Data
Stocks Network Analysis of KLSE: A Multivariate Time Series Similarity Approach
A Bivariate GLM With Application
Research Using Structural Equation Modelling
Discriminant Validity Assessment: On Fornell & Larcker Criterion Versus HTMT
Criterion
Invariance Analysis for Establishing External Validity of Islamic Maternal
Attachment Model
Tracking Socio-Demographic Changes in Malaysia
Employment of Women in Malaysia- Four Decades of Change
Family Support for Older Malaysians
Applied Statistics in Insurance
Risk Diversification in Insurers Under Stress Scenarios
Current Application of Statistical Techniques to The Malaysian Actuarial
Profession
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ISI-RSC 2014 ABSTRACT BOOK
Contributed Paper Sessions
CPS01
CPS02
CPS03
CPS04
CPS05
CPS06
Statistical Theory and Methodology I
A Note On Shrinkage Kernel Density Estimation
A Comparative Study On Different Class of Optimal Design Criteria
A Procedure to Attaining Non-Inferiority Margin
A Study On The Simultaneous Tests Under The Normality
Characterising Gumbel Autoregressive Process
Economics and Econometerics I
The Marginal Propensity to Consume Across Household Income Groups
An Econometrics Model for The Determinants of Manufacturing Production's
Volatility in Malaysia
Electricity Consumption and Economic Growth in Malaysia: A Multivariate Threshold
Co-Integration Analysis Approach
Wage Inequality and Trade Reforms in Malaysia: An Empirical Study
Statistical Theory and Methodology II
A Modified Rotnitzky- Jewell Criteria for Selecting Correlation Structure for
Generalized Estimating Equations
Generalized Concept of Relative Risk and Wider Applications of the Proportional
Hazards Model and the Kaplan-Meier Estimator
New Bivariate Zero-Inflated Negative Binomial Regression Models with Flexible
Correlation Structure
Extended Cox Regression Model in Case of Violation of the Proportionality
Assumption: Heaviside Function Approach
Utilization of a Known Coefficient of Variation in the Normal Variance Interval
Estimation Procedure
Economic and Econometerics II
Assessing the Operational Performance of Orissa Power Generation Corporation
Limited Through Slack Based Model
Developing An Algorithm for Making Profitable IPO Investment Decisions: Evidence
From Malaysia
Divergence of International and Domestic Prices of Imported Edible Oil In A Small
Open Economy: The Case of Bangladesh
Traffic Highway Development: An Analysis Based On Time Series Similarity Approach
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Statistical Theory and Methodology III
Objective Priors for the Zero-Inflated Model
On the Bayesian Estimation in Chain Binomial Epidemic Models
Duality Between Likelihood and Entropy In Bayesian Model Averaging
Exact Representation of Resampling Moments
A Criterion On Apportionment Methods Minimizing the Rényi’s Divergence
p. 112
p. 112
p. 113
p. 114
p. 114
Official Statistics
Tax Statistics: A New Addition to the Official Statistics
Temporary Redistribution of Allocation Fund
Using Principal Component Analysis in Index Construction: Application to
p. 115
p. 115
p. 116
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ISI-RSC 2014 ABSTRACT BOOK
Construction Material Prices and Building Permit Applications
Quality of the Regulatory Governance and Affordability on Economic and Information
Seeking Use of Facebook in A Developing Country
Big Data and Official Statistics in China
CPS07
CPS08
CPS09
CPS10
CPS11
CPS12
Statistical Theory and Methodology IV
A Note on Approximating ABC-MCMC Using Flexible Classifiers
A Note on the Bivariate Generalized Linear Model With Identity Link Functions
A Weighted Approach to Zero-Inflated Poisson Regression Models With Missing Data
In Covariates
Measuring Explanatory Variable Contributions in Generalized Linear Models
Estimation of Concordance Statistics for Logistic Regression Models: A Simulation
Study
Statistical Applications I
The Equilibrium Distribution of the Hwang-Green Substitution Model On A Circular
DNA
On the Estimation of Survival of HIV/AIDS Patients On Anti-Retroviral Therapy: An
Application to Interval Censored Data.
A Statistical Model for Pre-Post Studies With Continuous Bounded Outcome Scores
Multivariate Approach to Modelling Clinical Trials Data
Economics and Finance
Network Analysis of Currency Exchange Market: A Multivariate Time Series Approach
Economic Sectors Network At Kuala Lumpur Stock Exchange
Reaction of Interest Rate to Inflation Gap, Output Gap and Exchange Rate: Evidence
from ASEAN Countries
Effects of Climate Shocks to Philippine International Trade
Statistical Applications II
Risk-Adjusted Cumulative Sum Charting Procedure Based On Multi-Responses
Generalized Variance Chart: Root Causes Analysis
On The Use of Multivariate Control Charts for Monitoring Television Ratings
Power Analysis for Detecting Interaction Effects in Heteroscedastic Factorial Designs
Auto-correlated Process Control: A Geometric Brownian Motion Approach
Statistics in Social Sciences
Statistics Education Research in Malaysia and The Philippines: A Comparative
Analysis for Future Directions
Learning Points in Producing Business Statistics As International Accounting Standard
Adopts
Forecasting Admissions of Comsats Institute of Information Technology (CIIT)
Application of Binary Logistic Regression On Undergraduate Result Data (A Case
Study of Abia State Polytechnic, Aba, Abia State Nigeria)
Relationship Between Self-Efficacy and Quality of Life: Female Teachers in Sarikei,
Sarawak
Statistical Applications III
A Regionalized Approach in Modeling Tropical Rainfall Process Using Gamma
Distribution
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ISI-RSC 2014 ABSTRACT BOOK
Daily Rainfall Modelling Using Zero-Inflated Gamma
Statistical Method for Examining Temperature Variation in South Asia From 1973 to
2013
Fitting Daily Rainfall Amount in Thailand Using Gamma Regression
Confidence Bands for Confidence intervals From Data of Two Parameters Exponential
Distribution Under Complete Censoring (Study Case: Waiting Time of Earthquake
Disasters in Indonesia at March 2013)
CPS13
CPS14
Statistics in Social Sciences II
Electoral Sociology - Egypt 2011-2013
Migration Survey 2013 in The Middle East: A Comparison Study
Woman Participation in Labor Force in Upper Egypt
Economic Assimilation of Rural-Urban Migrants: Evidence From Indonesia
Estimation of Cause Specific Death Rates for India By Data Triangulation
Statistical Applications IV
On Cluster Surveillance of Malnutrition Prevalence and Hunger Gaps in Kazaure Local
Government Area of Jigawa State, North-Eastern Nigeria
Multidimensional Poverty in Indonesia: A Spatial Analysis Using Geographically
Weighted Regression (Gwr)
The Spatial Extent of Land Use Externalities in the Fringe of Jakarta: Spatial
Econometrics Analysis
Potential Environmental Impacts of Lands Drowning Along the Northern Nile Delta
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ISI-RSC 2014 ABSTRACT BOOK
INVITED SESSIONS
IS02: TECHNIQUES AND APPLICATIONS OF VISUALIZATION FOR
EXPLORATORY DATA ANALYSIS
A Dynamic Graphical Software Called TongGrami for 4-9th Students
Jung Jin Lee*
Soongsil University, Seoul, Korea
[email protected]
Recent development of IT and network technology has enabled us to develop many softwares
for statistical education. An international classroom project for the 4-9th grade students known
as census@school has now made it possible to share data, ideas and experiences in learning
and teaching statistics. A software called TinkerPlots, www.keypress.com/x5715.xml, has been
developed for this project and it includes dynamic graphs, useful examples for students, and
simple data analysis. TinkerPlots is a good software for students, but we feel its operation is not
easy and examples do not suit Korean students. Also it is not a free software. We have
developed a dynamic graphical software called TongGrami for two years which suits for Korean
students, hopefully for all children in the world. TongGrami enables students to plot all graphs in
the 4-9th grade textbooks using the raw data and/or summarized data. The graphs are linked
dynamically each other and the masking function is possible between data sheet and graphs.
Conditioned graphs by using either a discrete or continuous variable enable students to explore
simple data analysis. TongGrami is developed by Java to suit the mobile environment such as
the android system. An international collaboration for developing this kind of software would be
possible.
Key Words: Statistics Education, Dynamic Graphs, Statistical Software
Examples of Matrix Visualization for Exploratory Data Analysis (EDA)
Chun-Houh Chen
Academia Sinica, Taiwan
[email protected]
John Tukey (EDA, 1977) lectured us “It is important to understand what you CAN DO before
you learn to measure how WELL you seem to have DONE it.” Data analysts and statistics
practitioners nowadays are facing difficulties in understanding higher dimensional data with an
increasingly complex nature while conventional graphics/visualization tools do not meet current
needs. It is the responsibility of statisticians to come up with graphics/visualization environment
that can help users understand what one CAN DO with complex data generated from modern
techniques and sophisticated experiments. This paper summarises methodological works on
matrix visualization for EDA of different data types (continuous, ordinal, binary, nominal,
symbolic interval, etc.) with application works on matrix visualization for various scientific studies
(biological experiments, medical projects, educational studies, social surveys, etc.).
Key Words: Matrix Visualization, Exploratory Data Analysis
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ISI-RSC 2014 ABSTRACT BOOK
IS03: COMPUTATIONAL STATISTICS
Integrating Sampling Weights with Cognitive Diagnosis Models
Kevin Carl P. Santos*
University of the Philippines, Diliman, Quezon City, Philippines
[email protected]
Martin Augustine B. Borlongan
University of the Philippines, Diliman, Quezon City, Philippines
Large-scale educational assessments usually involve complex sampling designs. As a typical
result, the data gathered are clustered with unequal selection probabilities for examinees from
different subpopulations or strata. Failure to incorporate the complex sampling design in the
estimation procedure may lead to biased parameter and standard error estimates. This paper
examines incorporating the sampling weights in the estimation of the parameters of a cognitive
diagnosis model. The study investigates the incorporation of the sampling weights in maximizing
the marginal likelihood function of DINA (deterministic inputs, noisy “and” gate) model.
Key Words: Complex Sampling Design, DINA Model, Educational Assessment
Nonparametric Bootstrap Inference in a Multivariate Spatial-Temporal Model: A
Simulation Study
Abubakar S. Asaad*
Department of Epidemiology and Biostatistics, College of Public Health,
University of the Philippines Manila,
[email protected]
[email protected]
Erniel B. Barrios
School of Statistics, University of the Philippines Diliman
[email protected]
Nonparametric bootstrap inference in a multivariate spatial-temporal procedure is proposed to
verify two assumptions namely, constant multivariate characteristics across spatial locations
and constant multivariate characteristics across time points. The bootstrap normal confidence
intervals for the multivariate characteristics across spatial locations/time points were constructed
for the proposed test procedures. The results of the simulation studies indicate that the
proposed test procedures were able to correctly identify the true situation and are properly sized
for large balanced and/or unbalanced data and are powerful. Therefore, the tests are robust.
However, the test is not robust for small balanced and/or unbalanced data because of their
being less powerful.
Key Words: Coverage Probability, Robust, Spatial Locations, Temporal Locations
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ISI-RSC 2014 ABSTRACT BOOK
IS04: STATISTICS IN INDUSTRY: QUALITY AND MANUFACTURING
Statistics in Pharmaceutical Manufacturing...Its Business Value and Challenges
Chang Tee Chin
GlaxoSmithKline, Singapore
[email protected]
The manufacturing of Active Pharmaceutical Ingredients (APIs) typically involves multiple
stages of complex chemical reactions and crystallizations. The pharmaceutical industry is highly
regulated, with stringent requirements on process control and the quality of drug products
produced. In order to consistently produce product of high quality, pharmaceutical
manufacturing leverages heavily on process understanding, control and improvement activities.
These activities harness the large amount of data generated during API batch manufacturing;
each API batch can be associated with a broad range of data, from input material and reagent
quality, to plant processing parameters, to laboratory analytical testing results. In this talk, some
of the statistical inference tools commonly applied in pharmaceutical manufacturing, and their
associated business value, are presented. The statistical inference process that takes into
account technical and practical considerations is discussed. With the advent of continuous
manufacturing processes, an advanced technology that can result in a higher level of
productivity and output consistency, massive amounts of time-varying process data can be
generated, providing an even greater challenge in process understanding and control. An
overview of technical challenges of applying multivariate statistical modelling and control in a
continuous processing context is also provided.
Key Words: Active Pharmaceutical Ingredient, Data Integration and Visualization, Multivariate
Statistical Modelling and Control, Time-Varying Processing Parameters
A Negative Binomial Hurdle Model with Semi-nonparametric Random Effects for the
Study of Copper Hillocks Growth in Semiconductor Manufacturing
Ng Szu Hui
Department of Industrial & Systems Engineering, National University of Singapore, Singapore
[email protected]
Li Guilin
Department of Industrial & Systems Engineering, National University of Singapore, Singapore
[email protected]
Copper hillock formation is a major issue in copper interconnects used in complementary metaloxide-semiconductor (CMOS) manufacturing. They are formed during processing when copper
grows vertically due to heat, pressure and electrical factors, and can degrade device electrical
performance and reliability by perforating dielectrics or creating shorts between metal lines. Due
to their small size, they are extremely hard to detect. The purpose of this study is to develop a
better understanding of the process design factors on the hillocks growth mechanisms based on
preliminary physical understanding of the migration process, and improve the semiconductor
design to reduce shorting failures in the chIS. In an initial experiment, data was collected on Etest structures designed to test the value of leakage current between the metal layers in the
chip.
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Specially, in order to determine design features to minimize shorts, data on the number of short
currents (defects) were collected. However, due to the high quality characteristics of the
process, the denned response defects counts is characterized by excessive zeroes and overdispersion. To model this, a negative binomial hurdle model is adopted, and to account for the
large correlations between wafers and lots, random wafer and lot effects are incorporated into
the hurdle model. As the random effects observed are skewed, the typical normal random
effects model cannot be adopted. Instead, in this work, we propose a semi-nonparametric
approach to model the random wafer-to-wafer and lot-to-lot effects within the hurdle model. A
Monte Carlo EM algorithm is used to estimate the parameters of the model. Model verification is
discussed and initial insights from the model on hillocks growth will be presented.
Key Words: Copper Hillocks Growth, Hurdle Model with Random Effects, Semi-nonparametric
Distribution, Monte Carlo EM Algorithm
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ISI-RSC 2014 ABSTRACT BOOK
IS05: MULTILEVEL ANALYSIS OF SOCIAL SCIENCE DATA
The Impact of Social Capital on Individual Income in Malaysia: A Multilevel Modeling
Approach
Mohd Nasir Saukani
University of Malaya
[email protected]
Noor Azina Ismail
University of Malaya
[email protected]
Idris Jajri
University of Malaya
[email protected]
For decades-long, economists have been relying on the existing conventional capital (i.e.
physical capital, financial capital, human capital) in finding an exact explanation on factors that
contributes to income inequality. The scope of economic thinking should be broaden by taking
into account social capital i.e. the relational aspect (capabilities to interact and build
relation/networks) of economic players as another potential determinant of inequality. Theorists
and advocates believes that social capital acts as a lubricant to smooth the economic activity
and complement the existing conventional capital as an engine of economic growth, enhances
individual income/wellbeing and alleviate income inequality. In Malaysia, income inequality
remains an unresolved issue despite lot of efforts had been taken to tackle it. The aim of this
study is to analyze the impact of social capital on individual income level, where the outcome is
crucial in giving a broader perspective on factors that actually determine the prolong disparity of
income facing by Malaysian. Using data of 2443 individuals (head of household and working
household members) collected from field study in Kedah, Selangor, F.T. Kuala Lumpur, Johor
and Terengganu in 2012/2013, this study employed the multilevel modeling (MLM) technique,
the rarely used method in economic study. MLM which enable the influence of factors on
particular economic issue to be analyzed accordingly to the individual level and the group level
(working household and districts in the states sample respectively in this study) shows a
significance impact of social capital in influencing individual income level.
Key Words: Social Capital, Human Capital, Individual Income, Multilevel Modeling (MLM)
A Total-Education-Year Approach to Measure Human Capital -- Revision Results Based
on the 2010 Population Census in China
Qingyan Shi*
National Bureau of Statistics, Beijing, China,
[email protected]
Human capital is one of the key factors to economic growth. Lifetime labor income approach is
the popular method to measure human capital. Due to the lack of detailedinformation for income
categories, Total-Education-Year(TEY) approach is anacceptable alternative. Thispaper
demonstrates that if TEYapproach is used to calculatehuman capitalfor China based on the
official annual regular sampling surveys for the period of 2005 to 2009, it is found that
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underestimation existing to some extent. The causefor this underestimation is that the sampling
frame adjusted and new samples sampled according to the 1% population sample survey in the
year 2005 is not representative enough to education levels of the employed. Based on the 2010
population census data, this paper revised the TEY result from2005 to 2009 by usingthe
‘weighting-based-on-correlated-indexapproach’. The revision shows that the human capital of
employed based on official annual regular sampling surveys is underestimated by 3.4%-4.2%
comparedwith the revised results.
Key Words: Statistic Revision, Annual Regular Sampling
16
ISI-RSC 2014 ABSTRACT BOOK
IS06: SECURITIES STATISTICS COMPILATION AND STOCK MARKET
ANALYSIS
An Empirical Study for Testing the Stock Market Efficiency and Identifying Abnormal
Return Opportunities
Merve Artman
Central Bank of Turkey, Ankara, Turkey
[email protected]
Murat Artman
Central Bank of Turkey, Ankara, Turkey
[email protected]
The efficient market theory states that active management in the long term is a waste of money
and that an investor is better off placing assets into every type on index fund and should take a
passive strategy approach to investing. However, investors can outperform the market and
identify abnormalities that give them a trading advantage. This paper studies the stock return
data of Herbalife Ltd., a NASDAQ Company, from 2008-2012 including the unexpected event
day that caused the share price to fall down 19.94%. The contribution of this paper is to show
normality, event study, monthly and January effects on stock return performance with using
econometrical and statistical tests. First we ask, does the information arrive linearly to the
market or do investors react linearly to its arrival? Our results indicate that the stock return data
of the company is not normally distributed and there is a possibility of earning abnormal returns
by investors. The second question we ask, does the investor’s reaction to the market last longer
than the event day itself? Our results suggest that the event effect did not absorb on the same
day of event. Our evidence suggests that a trader can profit from shorting a stock several days
after a major negative event has occurred. We also ask if there are monthly or January effects
on the performance of stock returns of the company. We observed that there are significant
gains to be had from moving into the stock in the beginning of the month and moving out of the
stock by the end of the month and repeating that process over and over. However, there is no
statistically significant evidence that unusually high returns amass in the first couple of days of
January, while the return for the rest of the year statistically indistinguishable from zero.
Key Words: Stock Market Efficiency, Normality, Event Study, Market Anomaly, January Effect
17
ISI-RSC 2014 ABSTRACT BOOK
An Overview of the Current Status of the Securities Statistics in Turkey
Burcu Erik
Central Bank of the Republic of Turkey, Ankara, Turkey
[email protected]
Recent international recommendations to improve securities statistics once more highlighted the
significance of the use of securities statistics in attaining a comprehensive understanding of the
functioning of the financial markets for financial stability analyses. This paper summarizes the
methodology and assumptions employed in compilation of those statistics in Turkey, as well as
the challenges encountered and solutions employed to overcome those challenges. Providing a
brief description of the securities database in Turkey, it concludes with an example on the use of
securities statistics in dwelling into the underlying causes of market developments.
Key Words: Securities Statistics Database, Compilation of Securities Statistics
18
ISI-RSC 2014 ABSTRACT BOOK
IS07: ADDRESSING CHALLENGES IN COMPILING BALANCE OF
PAYMENTS STATISTICS
Addressing Challenges in Compiling Balance of Payments Statistics
Jacques Fournier
Banque De France, France
[email protected]
Balance of payments statistics have recently undergone a major change with the
implementation of the BPM6 methodology. These global standards take into account important
developments in the international economy, such as globalization, the development in financial
markets and the multiplication of complex investment operations. Thanks to the new
methodology, balance of payments and international investment position statistics are more
consistent with national accounts and should improve the early detection of macro-economic
imbalances.
In order to keep these statistics both accurate and relevant in a changing economic and
financial landscape, compilers face several challenges. They have to ensure that their sources
and compilation methods allow them to maintain data quality, which is a challenge in itself given
the very detailed breakdown level required by the new Manual. They also need to take into
account the economic and financial developments, for instance the generalization of crossborder production processes or the accrued role of shadow-banking in financial operations. New
expectations, such as the emergence of the analysis of trade in value added (TiVA) or the use
of detailed data on cross border claims and liabilities, with breakdowns by currencies, maturities
or sectors have also to be met.
To answer these new requests, compilers generally need to obtain more disaggregated data
from economic agents, including from non-financial enterprises. This often in turn poses a
problem of reporting burden. Using surveys and statistical methods to infer broader
measurements from in-sample results are possible answers. An increasing use of these
techniques can however be demanding. New technological avenues, such as the use of Big
Data softwares, can also be worth being explored.
Finally, a clear and consistent communication strategy appears necessary to convince
respondents of the prominent interest of balance of payments.
Key Words: Balance of Payments, Early Detection, Macro-economic Imbalances, Trade in
Value Added
Measuring Services Trade in Malaysia: Meeting Statistical Challenges
Siti Asiah Ahmad
Department of Statistic Malaysia
[email protected]
The uniqueness characteristics of services and the needs to provide consistent and accurate
information lead to the challenges in measuring Statistics on International Trade in Services
(ITS) to Malaysia. In situations where a tendency of cross border mobility on factors of
production occurs, it is also a great challenge, especially to produce the comprehensive and
reliable data as needed for the establishment of an effective policy towards high-income
country. This paper enlightens the issues and challenges in compiling SITS particularly
connected to Statistical coverage and data sources, adaption of new international Standard of
19
ISI-RSC 2014 ABSTRACT BOOK
BPM6 and venturing new statistical method and estimation. It was acknowledged that Manual
on Statistics of International Trade in Services (MSITS) and Balance of Payments Manual Sixth
Edition (BPM6) provides the conceptual framework and guidance to cope with the emerging
developments in relation to the rest of the world accounts. The issues and challenges are
identified mostly arises from the dynamic changes in doing business, locally and internationally.
The compiler will be better equipped with the vital insights by deliberating this matter intensively.
Key Words: Statistics on International Trade in Services (SITS), Balance of Payments
20
ISI-RSC 2014 ABSTRACT BOOK
IS09: STATISTICS IN MACHINE LEARNING AND APPLICATIONS
Reduced Kernel Sliced Inverse Regression
Su-Yun Huang
Institute of Statistical Science, Academia Sinica, Taiwan
[email protected]
Many kernel-based learning algorithms have the computational load scaled with the sample size
due to the column size of a full kernel Gram matrix. To reduce the computational load, a
Nyström low-rank approximation is introduced. It uses a reduced kernel matrix with a much
smaller column size. We will then discuss its application to the kernel sliced inverse regression
for pattern recognition. Some theoretical properties as well as computational aspects will be
presented. (This talk is based on joint work with Y.R. Yeh, Y.J. Lee, L.B. Chang, Z.D. Bai and
C.R. Hwang.)
Key Words: Kernel Method, Reduced Kernel, Nyström Low-Rank Approximation, Sliced Inverse
Regression
Bayesian Variable Selection for Finite Mixture Model of Linear Regressions
Ray-Bing Chen
[email protected]
We propose a Bayesian variable selection method for fitting the finite mixture model of linear
regressions. The model assumes that the observations come from a heterogeneous population
which is a mixture of a finite number of sub-populations. Within each sub-population, the
response variable can be explained by a linear regression on the predictor variables. If the
number of predictor variables is large, it is assumed that only a small subset of variables are
important for explaining the response variable. It is further assumed that for different subpopulations, different subsets of variables may be needed to explain the response variable. This
gives rise to a complex variable selection problem. We propose to solve this problem within the
Bayesian framework where we introduce two sets of latent variables. The first set of latent
variables are membership indicators of the observations, indicating which sub-population each
observation comes from. The second set of latent variables are inclusion/exclusion indicators for
the predictor variables, indicating whether or not a variable is included in the regression model
of a sub-population. Variable selection can then be accomplished by sampling from the
posterior distributions of the indicators as well as the coefficients of the selected variables. We
conduct simulation studies to demonstrate that the proposed method performs well in
comparison with existing methods. We also analyze two real data sets to further illustrate the
usefulness of the proposed method.
Key Words: Bayesian, Linear Regressions, Variable Selection, Latent Variables
21
ISI-RSC 2014 ABSTRACT BOOK
Dimension Reduction and Visualization of Time Dependent Interval Data Using Sliced
Inverse Regression
Han-Ming Wu
Tamkang University, New Taipei City, Taiwan
[email protected]
The dimension reduction of the interval-valued data is one of the active research topics in
symbolic data analysis (SDA). The main thread has been focused on the extensions of the
principal component analysis (PCA) though. Instead of using PCA, the sliced inverse regression
can be employed as the alternative to reduce the dimensionalities of the interval-valued data. In
many real-life situations, it happened that some of the interval data were time oriented (or
dependent). That is, the interval is described by a starting value and an ending value of a time
period, and the starting value may be larger or less than an ending value. The classical interval
DR methods that ignored the time features of the intervals may cause a loss of information. In
this study, we are motivated to use SIR as a base algorithm to develop sliced-based SDR
methods for time dependent the interval-valued data. In addition of the symbolic-numericalsymbolic approaches, we utilize the linear interpolation and the cubic spline interpolation for
sufficient dimension reduction and visualization of the time dependent interval data. The
proposed method considers the time dependency in intervals that can provide the appropriate
interpretations of results than those based on the single-valued or the classical interval-valued
data. We conducted a study on a microarray data of a yeast cell cycle. The results have shown
that the proposed method can reveal the insight structure of genes in the 2D factorial axis. We
discussed the statistical properties of the proposed method. We also investigated the further
applications of the found features for the classification, clustering and regression problems to
the real world data and microarray gene expression data. The comparisons with those obtained
with PCA will be also examined.
Key Words: Cubic Spline, Data Visualization, PCA, Sliced Inverse Regression, Sufficient
Dimension Reduction, Time Dependent Intervals
22
ISI-RSC 2014 ABSTRACT BOOK
IS11: STATISTICAL METHODS IN INDUSTRY: RELIABILITY
Warranty Cost Analysis: Geometric Operational and Repair Times
Stefanka Chukova
Victoria University of Wellington, New Zealand
[email protected] or [email protected]
Richard Arnold
Victoria University of Wellington, New Zealand
[email protected]
Yu Hayakawa
Waseda University, Tokyo, Japan
[email protected]
In this talk we start with a brief overview of Warranty Analysis. Then we discuss non-renewing
free replacement warranty policy assuming that the cost of each warranty claim depends on the
non-zero length of the repair time needed for its rectification. To model the process we are
interested in, we extend and modify the well-known alternating renewal process with cycles
consisting of the item's operational time followed by corresponding repair time. The modified
process, called geometric alternating renewal (GAR) process, reflects the geometrically
decreasing lengths of the consecutive operational times and geometrically increasing lengths of
the consecutive repair times. We present new theoretical results for GAR process over finite
time horizon. We use these results for cost analysis of non-renewing free replacement warranty
policy over the product warranty period as well as over the product lifecycle. Numerical
examples to illustrate our findings conclude this talk.
Key Words: Warranty Analysis, Geometric Process, Geometric Alternating Renewal Process
23
ISI-RSC 2014 ABSTRACT BOOK
Accelerated Degradation Analysis for the Quality of a System Based on the Gamma
Process
M.H. Ling
Department of Mathematics and Information Technology, The Hong Kong Institute of Education,
Tai Po, Hong Kong, China
[email protected]
K.L. Tsui
Department of Systems Engineering and Engineering Management, The City University of Hong
Kong, Kowloon Tong, Hong Kong, China
[email protected]
N. Balakrishnan
Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
L8S 4K1
[email protected]
As most systems these days are highly reliable with long lifetimes, failures of systems become
rare and consequently traditional failure time analysis may not be able to provide assessment
on the system reliability with precision. In this regard, the percentage to the initial value is an
alternate way of describing the system health. In this talk, accelerated degradation analysis that
characterizes health and quality of systems with monotonic and bounded degradation will be
presented. The maximum likelihood estimates of the model parameters are derived, based on a
gamma process, time-scale transformation, and a power link function for associating the
covariates. Then, methods of estimating the reliability, the mean and median lifetime, the
conditional reliability, and the remaining useful life of systems under normal use conditions are
all described. Moreover, based on the observed Fisher information matrix, approximate
confidence intervals for the parameters of interest are discussed. A model validation metric with
exact power is introduced. For an illustration of the proposed model and the methods of
inference developed here, a numerical example involving light intensity of light emitting diodes
(LED) is analyzed.
Key Words: Accelerated Degradation Analysis, Gamma Process, System Health, Remaining
Useful Life, Maximum Likelihood Estimates, Asymptotic Confidence Intervals
Parametric Inference for Multiple Repairable Systems under Dependent Competing Risks
Anupap Somboonsavatdee*
Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University,
Bangkok, THAILAND
[email protected]
Ananda Sen
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
[email protected]
Focus of this talk is on the analysis of repairable systems that are subject to multiple sources of
recurrence. The event of interest at the system level is assumed to be caused by the earliest
occurrence of a source, thereby conforming to a series system competing risks framework.
24
ISI-RSC 2014 ABSTRACT BOOK
Parametric inference is carried out under the Power Law Process model that has found
significant attention in industrial applications. Dependence among the cause-specific recurrent
processes is induced via a shared frailty structure. The theoretical inference results are
implemented to a warranty database for a fleet of automobiles for which the warranty repair is
triggered by the failure of one of many components. Extensive finite-sample simulation is carried
out to supplement the asymptotic findings.
Key Words: Dependent Failure Modes, Shared Frailty, Power Law Process, Recurrent Event,
Series System.
25
ISI-RSC 2014 ABSTRACT BOOK
IS13: MEASURING GOVERNMENT AND PUBLIC SECTOR DEBT
The Interest Rate Effects of Government Debt Maturity
Fabrizio Zampolli*
Bank for International Settlements
[email protected]
Jagjit S Chadha
University of Kent
[email protected]
Philip Turner
Bank for International Settlements
[email protected]
Federal Reserve purchases of bonds in recent years have meant that a smaller proportion of
long-dated government debt has had to be held by other investors (private sector and foreign
official institutions). But the US Treasury has been lengthening the maturity of its issuance at the
same time. This paper reports estimates of the impact of these policies on long-term rates using
an empirical model that builds on Laubach (2009). Lowering the average maturity of US
Treasury debt held outside the Federal Reserve by one year is estimated to reduce the five-year
forward 10-year yield by between 130 and 150 basis points. Such estimates assume that the
decisions of debt managers are largely exogenous to cyclical interest rate developments; but
they could be biased upwards if the issuance policies of debt managers are not exogenous but
instead respond to interest rates. Central banks will face uncertainty not only about the true
magnitude of maturity effects, but also about the size and concentration of interest rate risk
exposures in the financial system. Nor do they know what the fiscal authorities and their debt
managers will do as long-term rates change.
Key Words: Quantitative Easing, Sovereign Debt Management, Long-Term Interest Rate,
Portfolio Balance Effect
26
ISI-RSC 2014 ABSTRACT BOOK
IS14: INPUT OUTPUT TABLES AND LINKAGES TO ECONOMIC
GLOBALISATION
Input Output Tables and Tourism Satellite Accounts: Case Study of India
Ramesh Kolli
Member, National Statistical Commission, India and Independent International Consultant, New
Delhi, India
[email protected]
Poonam Munjal
National Council of Applied Economic Research, New Delhi, India
[email protected]
Tourism activity accounts for a sizable share in employment and Gross Domestic Product
(GDP) in most countries. However, measuring these economic variables related to tourism
poses challenges, as tourism is embedded in several economic activities and has not separately
been identified in the United Nations International Standard Industrial Classification (ISIC) of
economic activities. The possible route to measuring tourism is through the satellite accounting
framework suggested in the System of National Accounts (SNA), 2008. The UN World Tourism
Organisation (UNWTO) provides such a framework for tourism in its manual Tourism Satellite
Accounts: Recommended Methodological Framework, 2008 (TSA:RMF 2008). The TSA:RMF
facilitates estimating output and employment that is directly related to tourism. These direct
effects only take into account the immediate effects of the additional demand (tourism internal
consumption) of tourism on production processes in terms of additional supply of goods and
services, and additional value added and its components. However, the suppliers of this
additional demand require additional inputs from other producers due to inter-industry linkages.
This leads to indirect effects of additional demand for tourism, which can be estimated through
input-output quantity and price models. We present in this paper, the approach adopted in
preparing direct and indirect effects of tourism demand in the Indian economy using input-output
models, while carrying out the study done in the National Council of Applied Economic
Research (NCAER) on India’s Second Tourism Satellite Account (TSA) for the year 2009-10
that was commissioned by the Ministry of Tourism, Government of India. The study reveals that
tourism’s direct share in India’s GDP is 3.7 per cent and in employment, it is 4.4 per cent. By
including indirect effects, these shares go up to 6.8 per cent and 10.2 per cent respectively.
Key Words: Tourism Industry, Supply and Use Table, Indirect Effects, Output Multiplier
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ISI-RSC 2014 ABSTRACT BOOK
Development of Inter-Country Input-Output Model: Trade in Value-Added and Other
Globalization Analysis
Norihiko Yamano
Directorate for Science, Technology and Innovation, OECD, Paris, France
[email protected]
The inter-country input-output model has regained the attentions from various policy areas with
the increased participation of countries in the global production networks.
The conventional analytical frameworks based on the single country database are not sufficient
to perform effective empirical analyses for understanding various international agenda since
economic integrations with neighboring countries have increased the magnitudes of
international spillover and feedback effects. This presentation discusses the statistical
challenges and methodology behind recently estimated Trade in Value-Added indicators (TiVA).
Key Words: Inter-Country Input-Output Table, International Spillover Effects, Trade In ValueAdded
Regional Input Output Table: Inter-national Dependency in GMS Economy based on
bilateral IOT in the Case of Thailand and Vietnam 2000
Kwangmoon Kim
Graduate School of Management, Kyoto University,
[email protected]
Mr. Francisco Secretario, Mr. Bui Trinh, Mr. Hidefumi Kaneko
Association of Regional Econometrics and Environmental Studies (AREES)
This paper attempts to measure and analyze the interdependent economic relations between
the countries of Thailand and Vietnam, made possible by constructing a bilateral Input-Output
Table (IOT) linking the said two countries. One interesting observation of the results is the
multiplier effect of export demand on the import requirements in production. While the import
content of the production of export-oriented commodities cannot be directly measured from the
I-O table, impact analysis revealed that production of export goods and services in Thailand was
found to be more import-dependent than in Vietnam’s. It can thus be concluded that, in terms of
net foreign exchange earnings, which is estimated as the difference between gross export
receipts and calculated import “leakages”, appeared to be relatively more beneficial to
Vietnam’s economy than to Thailand’s.
Key Words: International IOT, GMS, Cross-Border Economy
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ISI-RSC 2014 ABSTRACT BOOK
IS15: APPLICATION OF STATISTICS IN ISLAMIC BANKING AND
FINANCE
Identifying the Determinants of the Shariah Non-Compliance Risk via Principal Axis
Factoring and Structural Equation Modelling with Proposed Accounting Technique as a
Risk Measurement
Muhammad Arzim Naim,
Universiti Kebangsaan Malaysia
[email protected]
Saiful Azhar Rosly
International Centre for Education in Islamic Finance (INCEIF), Malaysia
[email protected]
Mohamad Sahari Nordin
International Islamic University Malaysia
[email protected]
The objective of this study is to investigate the factors affecting the rise of Shariah noncompliance risk that can bring Islamic banks to succumb to monetary loss. Prior literatures have
never analyzed such risk in details despite lots of it arguing on the validity of some Shariah
compliance products. The Shariah non-compliance risk in this context is looking to the
potentially failure of the facility to stand from the court test say that if the banks bring it to the
court for compensation from the defaulted clients. The risk may also arise if the customers
refuse to make the financing payments on the grounds of the validity of the contracts, for
example, when relinquishing critical requirement of Islamic contract such as ownership, the risk
that may lead the banks to suffer loss when the customer invalidate the contract through the
court. The impact of Shariah non-compliance risk to Islamic banks is similar to that of legal risks
faced by the conventional banks. Both resulted into monetary losses to the banks respectively.
In conventional banking environment, losses can be in the forms of summons paid to the
customers if they won the case. In banking environment, this normally can be in very huge
amount. However, it is right to mention that for Islamic banks, the subsequent impact to them
can be rigorously big because it will affect their reputation. If the customers do not perceive
them to be Shariah compliant, they will take their money and bank it in other places. This paper
provides new insights of risks faced by credit intensive Islamic banks by providing a new
extension of knowledge with regards to the Shariah non-compliance risk by identifying its
individual components that directly affecting the risk together with empirical evidences. Not
limited to the Islamic banking fraternities, the regulators and policy makers should be able to
use findings in this paper to evaluate the components of the Shariah non-compliance risk and
make the necessary actions. The paper is written based on Malaysia’s Islamic banking
practices which may not directly related to other jurisdictions. Even though the focuses of this
study is directly towards to the Bay Bithaman Ajil or popularly known as BBA (i.e. sale with
deferred payments) financing modality, the result from this study may be applicable to other
Islamic financing vehicles.
Key Words: Shariah Non-Compliance Risk, Principal Axis Factoring, Structural Equation
Modelling, Risk Measurement
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ISI-RSC 2014 ABSTRACT BOOK
Does profit sharing provide market discipline in Islamic banking system?
Moutaz Abojeib and Omar Alaeddin
PhD Candidates, International Centre for Education in Islamic Finance (INCEIF), Malaysia
[email protected]
Prof. Simon Archer, Prof. Rifaat Ahmed Abdel Karim,
Adjunct Professors at INCEIF, Kuala Lumpur, Malaysia
Dr. Mohd. Eskandar Shah Mohd. Rasid.
Assistant Professor and Deputy Dean of Graduate Studies at INCEIF, Kuala Lumpur, Malaysia
Market discipline is one of the main pillars for stability and resiliency in banking system (Basel II,
2004). The mechanism of market discipline primarily relies on the role of depositors who receive
timely information and act accordingly through their respective accounts. Empirical evidence
shows the presence of market discipline, whereby non-insured depositors react to risk factors of
the bank accordingly either by withdrawing their deposit (quantity mechanism) or demanding
higher return (price mechanism). In tandem with conventional banking system, Islamic banking
also emphasize on market discipline, signified by the global standard number 4 issued by
Islamic Financial Services Board in 2007. However, unlike conventional banking, market
discipline in Islamic Banking is conjectured to work via profit-loss sharing system. That is, the
role of Profit Sharing Investment Account (PSIA) holders who are exposed to the variability of
profit generated from their investment. The intriguing question of whether PSIA would be more
effective and efficient in implementing market discipline remains an on-going debate. Even there
is no empirical study attempts to address this issue. Therefore, we perform empirical research
to discover it.
In this study, we use panel data of 44 Islamic banks across different regions to research the
presence of market discipline in the global Islamic banking system focussing on the behaviour
of the PSIA holders and their rule on governance of the Islamic banks. This study applies GMM
panel technique and finds weak relationshIS between the behaviour of PSIA holders and the
risk variables of the Islamic banks (measured by: Capital adequacy, Asset quality, Management,
Earnings or Liquidity). This implies negligible effect of the current profit sharing system in
exercising market discipline. This might indicates that PSIA holders remain unaware of bank’s
risks as well as low quality of disclosure process from Islamic banks. Nevertheless, our
empirical results for Malaysia indicate different conclusions, suggesting that PSIAH observe and
correspond to the changes in the asset quality risk as well as to the changes in liquidity risk by
quantity mechanism.
Key Words: Market Discipline, Islamic Banks, Profit Sharing Investment Account Holders, Price
Mechanism, Quantity Mechanism.
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ISI-RSC 2014 ABSTRACT BOOK
IS16: STATISTICAL INFERENCE WITH DEPENDENT DATA
Linear Mixed Models Using Longitudinal Linked Data
Klairung Samart
Prince of Songkla University, Songkhla, Thailand
[email protected]
Ray Chambers
University of Wollongong, Wollongong, Australia
[email protected]
Linked data sets are particularly useful in many areas such as epidemiology, health,
demography and sociology. One problem arising from linking process is the occurrence of
linkage errors. Although statistical methods for linking data sets are now well established and
the research on impact of linkage errors on analysis of linked data has been carried out, it was
mainly focused on linked records from two distinct data sets. In this research we develop
unbiased regression parameter estimates when fitting a linear mixed model to longitudinal
linked data where registers are linked over time. Furthermore, we develop appropriate
modifications to standard methods of variance components estimation in order to account for
linkage error. In particular, we focus on three widely used methods of variance components
estimation: analysis of variance, maximum likelihood and restricted maximum likelihood.
Simulation results and application on real life data show that our estimators perform reasonably
better than standard estimation methods that ignore linkage errors.
Key Words: Analysis of Variance, Linkage Error, Longitudinal Analysis, Maximum Likelihood,
Restricted Maximum Likelihood, Weighted Least Squares
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ISI-RSC 2014 ABSTRACT BOOK
Approximation of Bayesian Predictive P-Values with Regression ABC
David Nott
National University of Singapore
[email protected]
In the Bayesian framework a standard approach to model criticism is to compare some function
of the observed data to a reference predictive distribution. The result of the comparison can be
summarized in the form of a p-value, and it's well known that computation of some kinds of
Bayesian predictive p-values can be challenging.
The use of regression adjustment
approximate Bayesian computation (ABC) methods is explored for this task. Two problems are
considered. The first is the calibration of posterior predictive p-values so that they are uniformly
distributed under some reference distribution for the data. Computation is difficult because the
calibration process requires repeated approximation of the posterior for different data sets under
the reference distribution. The second problem considered is approximation of distributions of
prior predictive p-values for the purpose of choosing weak informative priors in the case where
the model checking statistic is expensive to compute. Here the computation is difficult because
of the need to repeatedly sample from a prior predictive distribution for different values of a prior
hyperparameter. In both these problems we argue that high accuracy in the computations is not
required, which makes fast approximations such as regression adjustment ABC very useful.
We illustrate our methods with several examples.
Key Words: Bayesian Predictive P-Values, Bayesian Computation Methods
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ISI-RSC 2014 ABSTRACT BOOK
IS17: ECONOMETRIC AND STATISTICAL METHODS FOR ECONOMIC
AND BUSINESS DECISION-MAKING
Fractional Multi-response Logit Regression Estimation of SMEs’ Financing Decisions
Myint Moe Chit
Nottingham University Business School, Malaysia Campus Semenyih, Malaysia
[email protected]
The growth of small firms is considered to be essential to the growth process of an economy. A
widely held belief is that Small and Medium Enterprises (SMEs) play a major role in
entrepreneurship, job creation and innovation. One of the major factors that adversely affect
SMEs growth and survival, however, is the lack of access to finance. This paper examines how
firm-specific, macroeconomic and institutional factors influence financing choices of SMEs in 48
emerging and transition economies using two-part fractional probit regression and fractional
multi-response logit regression models. Our findings indicate that firm size, owners’
concentration, institutional environment, macroeconomic stability and banking sector
development are important factors for SMEs financing decisions. Specifically, smaller and older
SMEs and SMEs in low growth, less financially developed countries rely more on retained
earnings and informal sources for their financing requirements. On the other hand, larger and
younger SMEs and SMEs in higher growth and stable macroeconomic environments are more
likely to borrow from and banks and non-bank financial institutions and issue new equities to
finance their investments.
Key Words: SMEs, Financing Decision, Institutional Environment, Transition Economies
To Ask or Not To Ask, That Is the Question
Hsin-Vonn Seow *
Nottingham University Business School, University of Nottingham Malaysia Campus, Jalan
Broga, 43500 Semenyih, Selangor, Malaysia
[email protected]
Lyn Thomas
School of Management, University of Southampton, Highfield SO17 1BJ, United Kingdom
[email protected]
In the process of applying for credit, applicants fill in a form and provide information for the risk
assessment process. In the current time and day, financial institutions are facing a saturated
consumer lending market, and hence falling acceptance rates for their personal financial
products. With data from the application forms, can such information be used to assess the
probability of a customer accepting the offer?
Acceptance of an offer is becoming crucially important since borrowers can apply’ to a number
of different companies at the same time, In some cases, the sale falls through because a more
suitable product was offered to the borrower from another financial institution. Lenders do not
want to make the application process too complicated, and with the growth in adaptive
marketing channels like the Internet and the telephone, one can make the questions asked
dependent on the previous answers. We investigate how one could develop such ‘‘adaptive’’
application forms; which would assess acceptance probabilities.
Key Words: Acceptance Probability, Modified CART
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ISI-RSC 2014 ABSTRACT BOOK
Pricing Private and Public Goods and Services through Valuation Methodologies and
Econometrics
Chuen-Khee Pek
Nottingham University Business School Malaysia
[email protected]
Non-market valuation methodologies like choice modelling and contingent valuation, and
several techniques of econometrics can be used to derive prices for both market and nonmarket goods and services. Such techniques can be applied to analyse choices in
environmental issues, where non-market goods and resources need to be properly priced to
reflect their relative values and, hence, facilitate sustainable policies and green economies. The
application can be used to value green products and services such like solid waste
management, hill recreational services, climate change, food security, mangroves, agricultural
multifunctionality, carbon footprints and even extended to banking products and services such
as green banking and loans. The paper highlights these environmetrics work studied in the
Malaysian context and shares the way forward in sustainable development through the key
principles of green economics, i.e. every aspect of sustainable economic activities.
Key Words: Non-Market, Valuation Methodologies, Environmetrics, Green Economics
34
ISI-RSC 2014 ABSTRACT BOOK
IS18: STOCHASTIC MODELLING IN BUSINESS AND INDUSTRY: ISBIS
SESSION
Component-based Predictive Path Modeling by means of Projections to Latent
Structures: Model Assessment and Interpretation Features with Business-related
Applications
Vincenzo Esposito Vinzi*
ESSEC Business School of Paris, Cergy-Pontoise, France
[email protected]
Laura Trinchera
NEOMA Business School, Rouen, France
[email protected]
Giorgio Russolillo
CNAM, Paris, France
[email protected]
Partial Least Squares Path Modeling (PLSPM) is a variance component-based method aimed to
model a network of (inter)dependence relationshIS between blocks of variables where each
block is summarized by a construct, i.e. a linear composite of its own manifest variables.
PLSPM suffers from some deficiencies in terms of coherence of the estimation algorithm with
respect to the direction of the links in the inner model that, apart from the specific case of the
so-called path weighting scheme, are actually overlooked. Therefore, the PLSPM estimation
process tends to amplify interdependence. In the search for optimally correlated constructs,
PLSPM misses to distinguish between the role of dependent and explanatory blocks in the inner
model. Moreover, at the level of the measurement model, in PLSPM the blocks are considered
either as uni-dimensional (in the so-called outwards-directed blocks, Mode A estimation mode
or reflective models) or as full-dimensional (in the so-called inwards-directed blocks, Mode B
estimation mode or formative models) while many real applications require the treatment of
blocks whose actual dimensionality needs to be searched in between these two extremes. In
order to overcome the limitations mentioned above, we will talk about a more suitable nonsymmetrical multi-dimensional approach based on an iterative algorithm that aims at explaining
variances through the extraction of components that are predictive of the manifest variables
related to dependent blocks and whose stability will be assessed through a bootstrap-based
procedure. For interpretation purposes, we will show how the projection to latent structures that
characterizes PLS regression may be used as a stabilizing estimation mode within the nonsymmetrical approach. Such a mode is effective in detecting and extracting those components
within each block that are relevant to explaining other blocks coherently with the prediction flow
specified by the inner model. Stability is enhanced by properly relating these components to
their own manifest variables so as to improve communality.
The features and performances of the proposed approach will be investigated on business
applications and then compared, through simulations, to related methods such as Regularized
Generalized Canonical Correlation Analysis and Generalized Structured Component Analysis.
Key Words: Partial Least Squares, Structural Equation Modeling, Multi-block Data Analysis,
Redundancy Analysis, Regression, Canonical Correlation Analysis
35
ISI-RSC 2014 ABSTRACT BOOK
A Bayesian Hierarchical Model for Negative Binomial Convolutions in Cluster Process
Models for Terrorist Activity
Gentry White
Queensland University of Technology, Brisbane, Queensland, Australia
[email protected]
Fabrizio Ruggeri
Consiglio Nazionale delle Ricerche, Italy
[email protected]
Michael D. Porter
University of Alabama, Birmingham, Alabama, USA
[email protected]
Self-exciting cluster models are specified as convolutions of two processes, a parent process
and a child process. Typically these are convolutions of Poisson processes, and the convolution
likelihood has an easy to compute closed form. For over dispersed data the negative-binomial
distribution is often preferred, but in this case the convolution likelihood is computationally
intense. The model in [2] uses parallel computing to address this issue modelling terrorist
activity in Colombia from 2000 to 2010. Results from the self-exciting model in [1] allow terrorist
events to have differing levels of either excitation or inhibition effects on future events, and the
aggregate effects of event history to vary over time. This is incorporated into a model for the
negative-binomial convolution cluster process model implemented in a hierarchical Bayesian
context that avoids the burden of computing the likelihood for the negative-binomial convolution.
Results are demonstrated for terrorist activity in Colombia from 2000-2010 and are compared
the results in [2]for both goodness of fit and computational ease.
Key Words: Point-Process Models, Bayesian Computation, Parallel Computing
On Bayesian Estimation of Thermal Diffusivity in Materials
Fabrizio Ruggeri
Consiglio Nazionale Delle Ricerche, Italy
[email protected]
Two approaches are presented to estimate the thermal conductivity or diffusivity of a
homogeneous material from the temperature evolution acquired in few internal points.
Temperature evolution is described by the classical one-dimensional heat equation, in which the
thermal conductivity (or diffusivity) is one of the coefficients. In the first approach noisy
measurements lead to a partial differential equation with stochastic coefficients and, after
discretisation in time and space, to a stochastic differential equation. Euler approximation at
sampled points leads to a likelihood function, used in the Bayesian estimation of the thermal
conductivity under different prior densities. An approach for generating latent observations over
time in points where the temperature is not acquired is also included. Finally, the methodology is
experimentally validated, considering a heated piece of polymethyl methacrylate (PMMA) with
temperature measurements available in few points of the material and acquired at high
frequency. In the second approach a Bayesian setting is developed to infer unknown
parameters that appear into initial-boundary value problems for parabolic partial differential
equations. The realistic assumption that the boundary data are noisy is introduced, for a given
prescribed initial condition. We show how to derive the global likelihood function for the forward
36
ISI-RSC 2014 ABSTRACT BOOK
problem, given some measurements of the solution field subject to Gaussian noise. Given
Gaussian priors for the time-dependent Dirichlet boundary values, we marginalize out
analytically the global likelihood using the linearity of the discretized solution. This approach is
fully implemented in the case of the heat equation where the thermal diffusivity is the unknown
parameter. We assume that the thermal diffusivity parameter can be modeled a priori through a
lognormal random variable or by means of a space-dependent stationary lognormal random
field. Synthetic data are used to carry out the inference. We exploit the concentration of the
posterior distribution of the thermal diffusivity, using the Laplace approximation and therefore
avoiding costly MCMC computations. Expected information gains and predictive posterior
densities for observable quantities are numerically estimated for different experimental setups.
Key Words: Thermal Diffusivity, Differential, Conductivity
37
ISI-RSC 2014 ABSTRACT BOOK
IS19: MEASURING MACRO-FINANCIAL RISKS IN THE POST-GFC
ENVIRONMENT
Global Interconnectedness in the Financial System
Kian-Teng KWEK
Associate Professor, Faculty of Economics & Administration, University of Malaya
[email protected]
Cho-Wai CHO
Senior Lecturer, Taylor’s University, Selangor
[email protected]
This study examines the interplay between finance and the real sector, by assessing how the
roles of the central banking (states), markets and enterprises were transformed from one of
fragility into anti-fragility after the Global Financial Crisis of 2008. First, broad patterns and
relationshIS of the global financial markets are established. Developments in the financial
sector have led to an expansion in its ability to spread risks – known as systemic risks, due to its
explosion of credit derivatives in the global unregulated markets. Second, paths analysis and
structural equation models are applied to understand the major factors that are underpinning
and undermining financial (in)-stability worldwide. This will shed some insight into the role of
unregulated markets in the global financial system, that is whether it is providing a valueincreasing or value-decreasing to global-GDP.
Key Words: Hidden Interconnections,
Increasing/Decreasing Global GDP
Negative
Interest
Rate
Policy,
Value
38
ISI-RSC 2014 ABSTRACT BOOK
IS20: COMPILATION AND USAGE OF FLOW OF FUNDS STATISTICS
Enhancement and Expansion of Japan’s Flow of Funds Accounts in Response to
International Recommendations after the Financial Crisis
Sayako Konno, Masahiro Higo
Bank of Japan
[email protected]
In response to the financial crisis in 2008, there are growing moves around the world to
enhance statistics in order to identify the buildup of risks and financial and economic
vulnerabilities that would not be captured through existing data. Accordingly, the Bank of Japan
(BOJ) worked to enhance and expand Japan’s Flow of Funds Accounts (J-FFA) and started to
release three new data series between 2011 and 2013. The first of them is “from-whom-towhom” data of debt securities and loans, which makes it clear from whom funds and risks move
to whom. The second is “Loans, Debt Securities, and Deposits by Maturity,” through which the
maturity mix of financial institutions’ major means of fund investment and fund raising is
identified. The third is “Amounts Outstanding of Securitized Products.” Securitization plays a
significant role in the transfer of risks in the United States and Europe through shadow banking
activities, meaning financial activities not conducted through banks. These data sets revealed
recent notable trends such as: (1) an increase in cross-border transactions; (2) a shift of creditor
from the public sector to the private sector; (3) differences in the maturity composition of fund
investment and fund raising among economic entities; and (4) a downtrend in the amounts
outstanding of securitized products.
Key Words:
Flow of Funds, Financial, Economic, Vulnerability
39
ISI-RSC 2014 ABSTRACT BOOK
Development of Financial Sectoral Accounts: Progress and Challenges
Bruno Tissot
Bank for International Settlements
[email protected]
The development of financial accounts is high on the international policy agenda. The BIS has
been particularly supportive of these international initiatives, especially through the activities of
its Irving Fisher Committee on Central Bank Statistics (IFC). The concept of financial accounts
has a variety of meaning. It is basically drawing from the traditional national accounts
framework, augmented to present information on financial flows and positions. In addition
several steps have been taken in recent years to refine some aspects of these statistics, with
the ultimate goal of building “Integrated sectoral financial accounts”. This information is
instrumental for supporting financial stability analyses, its development is presenting acute data
challenges.
Key Words:
Financial Sectoral Accounts, National Accounts
40
ISI-RSC 2014 ABSTRACT BOOK
IS21: MICRO-DATA FOR EFFICIENT AND COMPREHENSIVE
STATISTICAL SYSTEM
Using Micro-data to Support Analysis and Policy Decision at the Bank: My World View
Muhamad Shukri, Abdul Rani
Financial Surveillance Department, Bank Negara Malaysia
[email protected]
This brief paper discusses the use of micro-data to support analysis and policy decision at the
Bank, focusing on the availability of individual credit profile and survey data. After a brief
introduction, the paper describes these micro-data in greater detail and how information
gathered from these sources are then used by the Bank to support its macro prudential policy.
Key Words: Consumer Sentiment Survey, CCRIS (Central Credit Reference Information
System), Micro-Data
The Information Model at Banco de Portugal – Using microdata to face Central Banks’
challenges
João Cadete de Matos
Director, Statistics Department
[email protected]
The Porto Workshop on Integrated Management of Micro‐databases, organised in 2013 by the
Banco de Portugal (hereinafter referred to as the ‘Bank’), concluded that micro‐databases are
already very relevant for many central banks’ statistical systems and that they often are the
most important infra‐structure within those systems. This paper discusses how the Bank has
been exploring the statistical potential of a number of available micro‐databases, which cover
different areas of the economy and the financial system, with the aim of enhancing the
effectiveness and efficiency of its statistical system while keeping the respondents’ burden at an
acceptable level. The granular nature of such information, together with a good coverage of the
relevant population, offers increased flexibility as regards the compilation of new statistics and a
more rapid response to face Central Banks' challenges. One thing that we have learned with
the global financial crisis is that aggregate figures are not sufficient to fully grasp developments
in economic variables as they do not give us a perspective on the full distribution of values.
Quite the contrary, these data should be complemented with microdata, which enable exploring
the heterogeneity hidden behind aggregate numbers. In fact, in many situations, the tails of the
distribution provide the most important information, and that clearly explains why these data
became crucial in recent times. With this in mind, the Bank is revamping its information model,
including a streamlined governance structure, a revisited relationship management model and a
continually improving information architecture based on micro‐data and a Data Warehouse
(DW).
Key Words: Micro-database, Statistical System, Data Warehouse
41
ISI-RSC 2014 ABSTRACT BOOK
IS22: ENHANCING THE MALAYSIAN TAX SYSTEM
The Relationship between Tax Rates and Tax Revenue in Malaysia: Is There A Laffer
Curve?
Sherly George
Lecturer, Faculty of Management, Multimedia University Cyberjaya
[email protected]
Professor Dr Syed Omar Syed Agil
Director, Centre of External Programme, University Tun Abdul Razak, Kuala Lumpur
[email protected]
The current individual and corporate tax base rate imposed in Malaysia does not seem to
generate the best possible tax revenue at its maximum point thus indirectly affecting economic
growth. The importance of generating higher tax revenue is to finance government expenditures
over the years. Insufficient tax revenue will lead to government borrowings and severe
government debts over the years if this issue is not addressed immediately. The main objective
of this research is to determine optimum tax rate appropriate for both individuals and
corporates. With the optimum tax rate obtained, the Malaysian government is able to generate
maximum tax revenue for both individuals and corporates respectively. Optimum Tax Theory
models using the Laffer curve concept is used to estimate the tax rates for individual and
corporate where at this point the tax revenue is at its maximum point thus contributing to the
economic growth. Data of individual and corporate tax rates, tax revenue are gathered for over
33 years (1980-2013) from Data Stream, Department of Statistics, Bank Negara Malaysia and
World Bank. The data is analyzed using Ms Excel and R-Programming. This research will
enable the government to restructure the tax base as well as restructure the tax system in order
to increase higher tax revenue. Increased tax revenue enables government to fund escalating
expenditures and eliminate government debts due to increased government borrowings over the
years. Lower tax base will also increase individual purchasing power thus increasing
consumption and contributing to the economic growth. As for the corporate sector, decrease in
corporate tax rate boost investment generating higher corporate tax revenue which directly
increases Malaysian GDP. This paper derives the optimum tax rates for both individual and
corporate denoted as 1 Malaysia Optimum Individual Tax Rate (1MOITR) and 1 Malaysia
Optimum Corporate Tax Rates (1MOCTR) generating maximum individual and corporate tax
revenue.
Key Words: Tax Rates, Tax Revenue, Optimum Tax Rate, Laffer Curve
42
ISI-RSC 2014 ABSTRACT BOOK
Estimating the Size of the Hidden Economy in Malaysia?
Jeyapalan Kasipillai
Monash University Malaysia, Malaysia
[email protected]
As a result of increased economic activities during the last three decades, there has been a
significant increase in the size of hidden economy in numerous countries, including Malaysia.
Hidden economy is also referred to as the black economy, submerged economy, subterranean
economy or underground economy.
The hidden economy refers to activities that results in transactions (either in kind or for
payment) between individuals which are hidden from the authorities, principally tax departments
(Pyle, 1989). Essentially, the designation refers to those economic activities that should be
reported or measured by the techniques and conventions currently used for measuring
economic activity, but are not. These activities are hidden because those taking part in them
make private gain by keeping them concealed. These gains may take the form of evaded taxes,
non-compliance with costly regulation, income from prohibited and criminal activities, or
fraudulent receipt of various government benefits.
Economists, academicians, statisticians and tax authorities continue to debate the extent of the
problem of estimating hidden (or concealed) income, the reasons for its increase and what
could be done to curb it. Hidden income is invariably concealed from tax authorities. This
introduces a fundamental difficulty into the measurement of tax evasion. There are disputes or
disagreements about the definition of hidden income; terms used to explain these activities
(example: hidden income, concealed income or evaded income); estimation measurement
procedures; and use of these estimates in economic analysis. Unlike developed countries such
as the USA and Australia where tax information is made available for analyses, measuring
hidden income in Malaysia is problematic as revenue authorities simply do not release tax data.
As such, there is a need to commence a process whereby more tax data is released as is done
in certain developed countries and in some of these countries, there are freedom of information
laws in place. Consequently our study adopts a triangulation approach to analyse and detect
how taxpayers evade or “conceal” their income. Our qualitative study use a survey instrument
and target a specific group of highly knowledgeable professionals on where “concealment” of
income in Malaysia is concentrated and find out how these activities are carried out. As for the
quantitative approach, we collect related tax data (e.g., deferred taxes, taxes paid, and changes
to all assets and liabilities that have tax consequence) that are available in company annual
reports and Osiris data base for the period 2005-2010. The researchers expect to identify
nature or types of companies that take advantage of tax avoidance and also identify sectors of
the economy where evasion of income is perceived to be high. These findings would enable the
Government to recommend policies that would result in ways to raise revenue for
developmental purposes.
Key Words: Hidden Economy, Tax, Hidden Income
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ISI-RSC 2014 ABSTRACT BOOK
IS23: STATISTICAL METHODS IN LIFE SCIENCES
Estimating the Number of Neurons in Multi-Neuronal Spike Trains
Wei-Liem Loh
National University of Singapore
[email protected]
A common way of studying the relationship between neural activity and behavior is through the
analysis of neuronal spike trains that are recorded using one or more electrodes implanted in
the brain. Each spike train typically contains spikes generated by multiple neurons. A natural
question that arises is “what is the number of neurons  generating the spike train?". This talk
proposes a method-of-moments technique for estimating . This technique estimates the noise
non-parametrically using data from the silent region of the spike train and it applies to isolated
spikes with a possibly small, but non-negligible, presence of overlapping spikes. To gauge its
finite sample performance, the technique is applied to simulated spike trains as well as to actual
neuronal spike train data.
Key Words: Method-of-Moments, Mixture Distribution, Neuronal Spike Train, Spike Sorting
Association of Cardiovascular Responses with Fine Particle Air Pollutions in Beijing
Jing-Shiang Hwang
Academia Sinica, Taiwan
[email protected]
For concerns about the health of athletes and international visitors to 2008 Olympic Games in
Beijing, the government mitigated air pollution by relocating, limiting or temporarily closing highly
polluting, energy-intensive facilities in and around the city, and reducing vehicle usage by
elaborate traffic regulations. These air quality interventions, albeit temporary, encouraged
numerous investigations on air pollution and its biological effects before, during and after the
Games, and provided us a unique opportunity to assess the effect of reduction in fine particles
on cardiovascular responses. In this study, Bayesian approaches were used to identify fine
particulate matter (PM) sources and estimate their contributions to the ambient air pollution in
Beijing. The estimated contributions were brought into mixed-effects models as exposures for
examining the association of cardiovascular responses of the exposed mice in a sub-chronic
experiment. We will show how the alterations in cardiac parameters were closely related to
changes in Beijing ambient PM concentration and various pollution source concentrations.
Key Words: Cardiovascular, Air Pollution, Bayesian
44
ISI-RSC 2014 ABSTRACT BOOK
Dynamic Treatment Regimes for Chronic Health Conditions: An Overview of Statistical
Problems and Available Solutions
Bibhas Chakraborty
1. Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore
2. Department of Biostatistics, Columbia University, USA
[email protected]
Effective treatment of many chronic health conditions requires physicians to make
individualized, sequential decisions. To do this, each patient’s treatment (type, dosage, and
timing) is dynamically adapted over time based on the patient’s case history and evolving
disease state. Dynamic treatment regimes (DTRs) operationalize this idea via a sequence of
adaptive decision rules, and can serve as a data-driven decision support system for clinicians.
In recent years, there has been a surge of interest in developing and evaluating data-driven
DTRs from specially designed clinical trials called sequential multiple-assignment randomized
trials (SMARTs) as well as from various observational studies. Estimation and inference in this
arena presents novel and challenging methodological problems, since these estimators typically
involve non-smooth operations of the data. In this talk, we will discuss an overview of the field of
dynamic treatment regimes within the bigger paradigm of personalized medicine, including
major statistical problems and their available solutions. Simulated and real data examples will
be provided as appropriate
Key Words: Dynamic Treatment Regimes, Chronic Health Conditions, Sequential MultipleAssignment Randomized Trials
45
ISI-RSC 2014 ABSTRACT BOOK
IS24: DISTRIBUTIONS, MODELING AND INFERENCE (NEW TITLE)
Statistical Reliability Analysis of a New Mixed Generalized Gamma Distribution
Winai Bodhisuwan
Department of Statistics, Kasetsart University, Thailand
[email protected]
A new flexible alternative distribution to lifetime data analysis, namely the mixture generalized
gamma (MGG) distribution is introduced. The MGG distribution is obtained by mixing between
generalized gamma distribution and length biased generalized distribution. In this talk,
properties of the MGG distribution are presented. Some important reliability functions of the
MGG distribution are derived. Finally, applications of the MGG distribution in reliability analysis
are illustrated with real data and numerical examples are also includes.
Key Words: Life Time Distribution, Mixture Generalized Gamma (MGG) Distribution, Reliability
Analysis
Mixture Poisson Point Process: Assessing Heterogeneity in EMA Analysis
Nat Kulvanich
Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University,
Bangkok, Thailand
[email protected]
Stephen L. Rathbun
Department of Biostatistics, University of Georgia, Athens, USA
[email protected]
The times of repeated behavioral events can be viewed as a realization of a temporal point
process. Taking inspiration from the implementation of mixture models for the impact of timevarying covariates on recurrent events, we propose two different mixture models that expand
upon previous research for the use of Poisson process to analyze repeated event data. The
proposed estimators for model parameters will be shown to have the desirable asymptotic
properties. We illustrate our approach using data from an ecological momentary assessment of
smoking.
Key Words: Poisson Process, Mixture Models, Point Process, Ecological Momentary
Assessment, Smoking, Generalized Linear Mixed Models, Hierarchical Likelihood, Maximum
Likelihood Estimation
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ISI-RSC 2014 ABSTRACT BOOK
On the Two-Sample Behrens-Fisher Problem with Fewer Observations than the
Dimension
Zhang Jin-Ting
Department of Statistics and Applied Probability, National University of Singapore, Singapore
[email protected]
High dimensional data such as DNA microarray data are collected frequently in many modern
scientific fields. The number of observations is often much smaller than the dimension. In this
talk, we consider a two-sample Behrens-Fisher (BF) problem with fewer observations than the
dimension. In this case, the existing BF testing procedures for multivariate BF problems are no
longer applicable. To overcome this difficulty, we propose and study an L2-norm based test. We
show that the proposed test statistic has a distribution of a chi-square-type mixture. The
methods for approximating the null distribution are proposed. Simulation studies and real data
applications are used to illustrate the methodologies and compare the proposed test with some
existing testing procedures for multivariate BF problems.
Key Words: High-Dimensional Behrens-Fisher Problem, L2-Norm Based Test, Chisquare-Type
Mixtures, Two-Sample Test, Welch-Satterthwaite Chi-Square Approximation
47
ISI-RSC 2014 ABSTRACT BOOK
IS25: STATISTICS IN INDUSTRY: RECENT DEVELOPMENTS IN
STATISTICAL PROCESS CONTROL METHODS
The Variable Parameters X Chart with Estimated Process Parameters
Sin Yin Teh*
School of Management, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia
[email protected]
Michael Boon Chong Khoo
School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia
[email protected]
Min Xie
Department of Systems Engineering and Engineering Management, City University of Hong
Kong, Kowloon Tong, Hong Kong
[email protected]
Yuen Cheng
Department of Systems Engineering and Engineering Management, City University of Hong
Kong, Kowloon Tong, Hong Kong
[email protected]
In Statistical Process Control (SPC), the Shewhart X chart is widely used to monitor the quality
of processes in manufacturing and service industries because of its simplicity. The Shewhart X
chart is insensitive for detecting small and moderate process mean shifts, where the
employment of the variable parameters (VP) X control chart was suggested as a remedial
action. However, a major problem prevalent in existing studies on VP X control chart is the need
to assume that process parameters, i.e. the process mean and process variance, are known. In
reality, these parameters are usually unknown and are estimated from the incontrol Phase-I
data. The objective of this research is to explore how process parameter estimation affects the
performance of the VP X chart. Moreover, this research project will also propose the designs of
the VP X chart with estimated process parameters, based on the average run length (ARL) and
median run length (MRL) criteria, and compare its performance with the VP X chart having
known process parameters. Theoretical derivation of formulae via the Markov chain embedding
technique will be made to enable the ARLs and MRLs of the VP X chart with estimated process
parameters to be computed, followed by obtaining the statistical designs of the chart. The
Statistical Analysis System (SAS) software will be used in the analyses. The designs and
implementation of the VP X chart with estimated process parameters will be demonstrated with
examples.
Key Words: Markov Chain, Run Length, Statistical Process Control, Unknown Parameters
48
ISI-RSC 2014 ABSTRACT BOOK
Control Charts for Measurements with Known or Estimated Periodicity
M. Xie
City University of Hong Kong, Hong Kong
[email protected]
In many industrial processes, measurements with some kind of periodicity are often
encountered. To monitor this type of process, a type of control chart, called circle chart, has
been proposed. It enables the user to better monitor data with periodicity. Instead of plotting
data as run chart, observations are plotted around a circle. It presents a method to make use of
the periodicity information and a simple way for decision making. In this paper, some
approaches to further adopt this approach will be presented. Some data normalization
techniques are suggested for the monitoring. Comparison of three transformation techniques is
shown and some possible future works are outlined, especially for the case of unknown
periodicity.
Key Words: Control Charts, Known or Estimated Periodicity
A Phase-II Nonparametric CUSUM Chart for Joint Monitoring of Location and Scale
A. Mukherjee
Indian Institute of Management Udaipur; OM, QM& IS, Udaipur, Rajasthan, India.
E-mail: [email protected]
M. Marozzi
Dipartimento di Economia e Statistica, Università della Calabria, Rende (CS), Italy
E-mail: [email protected]
S. Chowdhury
Indian Institute of Management, Kozhikode, QM & OM, Kerala, India
E-mail: [email protected]
Recently, Chowdhury et al. (2014) proposed a single distribution-free Shewhart-type control
chart based on the Cucconi (1968) test statistic for monitoring shift in the unknown location and
scale parameters of a process distribution simultaneously. Several recent researches
demonstrated that the CUSUM type charts perform better than the Shewhart-type charts under
small and persistent shift. In the present work, we develop a phase II distribution-free CUSUM
chart based on the Cucconi statistic, referred to as CUSUM-Cucconi (CC) chart. Nonparametric
nature of the Cucconi statistic ensures that all the in control (IC) properties of the proposed
chart remain invariant and known for all continuous process distributions. Control limits are
tabulated for implementation-of the chart. The IC and out of control (OOC) performance of the
chart are thoroughly investigated in terms of the average, standard deviation, median and some
percentiles of the corresponding run length distributions. A detailed comparison with the
Shewhart-type Cucconi and Lepage charts as well as the CUSUM Lepage chart is presented.
The proposed chart is illustrated through two real data sets.
Key Words: Cucconi Statistic, Average Run Length, Upper Control Limit, CUSUM Cucconi
Chart, Nonparametric, Monte-Carlo Simulation, Statistical Process Control.
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ISI-RSC 2014 ABSTRACT BOOK
IS26: INTERLINKED ECONOMIES IN PRACTICE
The Mediating Effect of International Financial Linkages on the Comparison of Optimal
Monetary Policy in the Generalized Taylor and Multiple Calvo Economies
Yue Jiang
International Business School Suzhou, Xi’an Jiaotong Liverpool University
[email protected]
+86(0)51288161703
This paper introduces multiple sectors in a range of price-setting DSGE models that allows
heterogeneous price stickiness consistent with the micro data: the generalized Taylor economy
(GTE), generalized Calvo economy (GCE) and Multiple Calvo economy (MCE). Comparing
these frameworks, we found that the GCE framework can achieve the pare to optimum. The MC
model generates lower welfare loss than GTE, due to lower sectoral price dispersion. Further, a
simple interest rule that responds to sectoral price inflation attaches policy weights to the
sectors similar to the sectoral shares in a GTE, whereas the price stickiness plays a more
important role in determining the policy weights in a MC model. However, the welfare gain of
such a policy to the interest rule that responds to the aggregate inflation is trivial.
Key Words: Heterogeneity, Inflation Targeting, Optimal Monetary Policy
South East Asian Financial Linkages: Insights from a Global VAR
Simon Rudkin
International Business School Suzhou, Xi’an Jiatong-Liverpool University, Suzhou, Jiangsu,
China 215123
[email protected]
Sen Min Wong
Bank Negara Malaysia, Kuala Lumpur, Malaysia
[email protected]
Asia is a continent which is seeing great change as the world order shifts ever eastwards toward
China. Emerging from the big crash of the late 1990s was a new series of linkages and financial
relationshIS ripe for exploration. Using a Global Vector Autoregressive (GVAR) model we
analyse these and the likely impacts of China as a growing neighbour. With data ranging up to
the end of 2012 this study is also able to better capture the impact of the 2008 global financial
crisis than past papers. Through simulations of shocks to equity prices in the USA, Singapore
and China we ask what insights can be gained for the region and what policymakers can learn
therefrom.
Key Words: Financial Linkages, Global VAR, Equity Prices
50
ISI-RSC 2014 ABSTRACT BOOK
Explaining Business Cycles: News Versus Data Revision
Bo Yang
Xi'an Jiaotong-Liverpool University
[email protected]
Paul Levine
University of Surrey
[email protected]
Joseph Pearlman
City University
[email protected]
This paper examines the claim by Schmitt-Grohe and Uribe (2009) that the presence of ‘news’
can better explain business cycle fluctuations, an idea that builds on the work of Beaudry and
Portier (2006). We assess this claim in a standard New Keynesian (NK) DSGE model and
compare it with an alternative that requires less extreme information assumptions (as in Levine
et al. (2010)) and utilizes revisions to real time data. Overall we conclude that data revisions can
be seen to be a proxy for news shocks, and in a marginal likelihood race, fit the data better. We
also find, based on standard moment criteria, including co-movements of key US economic
indicators, and validation using an identified DSGE-VAR, qualified empirical support for the
most basic NK models augmented with the revision processes.
Key Words: News Shocks, Imperfect Information, Real-Time Data, DSGE Model, Bayesian
Estimation
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ISI-RSC 2014 ABSTRACT BOOK
IS27: COMPILATION OF ECONOMICS INDICATORS THROUGH
SURVEY
FDI Survey on Special Purpose Entities (SPEs) in Luxembourg: the Case for Monthly
Granular Data
Paul Feuvrier
Banque Centrale Du Luxembourg
[email protected]
In 2012, Central Bank of Luxembourg set up an original FDI survey on SPEs, which are of major
importance in Luxembourg and on which the 2008 SNA introduced a detailed classification.
Quarterly Balance Sheets are reported along with monthly Security by Security positions.
Granular collected “stocks” make it possible to estimate transactions, FX and price effects
requested by the IMF Sixth Edition of Balance of Payments Manual (BPM6), thanks to the
implementation of ad-hoc editing and imputation rules. Last, investment incomes are derived by
combining the survey and SPEs P&L (administrative data), so that the burden is reduced for
companies.
Key Words: Foreign Direct Investments, Balance of Payments, International Investment
Position.
Measuring Uncertainty in the Financial Sector
Aytaç Erdoğan
Timur Hülagü
Central Bank of the Republic of Turkey
[email protected]
In this study, we provide a measure of uncertainty for financial institutions in Turkey by using a
panel data set from the Financial Services Survey. In particular, assuming that higher ex-ante
uncertainty leads to higher errors when predicting the future, we construct a measure derived
from expectation errors of financial institutions by comparing their survey responses about
expectations and realizations on their turnover. Results show that our uncertainty measure
increases significantly in some certain circumstances such as Fed's decision to decrease
monthly bond purchases in May 2013 and recent political turmoil in Turkey.
Key
Words:
Uncertainty,
Expectation
Errors,
Financial
Services
Survey
Data
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IS28: THE CHALLENGES OF USING SECONDARY DATA TO EXAMINE
LEVELS AND DISTRIBUTION OF HEALTH IN MALAYSIA
Measuring Universal Health Coverage in Malaysia
Chiu-Wan Ng
University of Malaya, Malaysia
[email protected]
Mohd Ridzwan Shahari
Institute for Health Systems Research
[email protected]
Jeevitha Mariapun
University Malaya Medical Centre
Noran Naqiah Mohd Hairi
University of Malaya, Malaysia
[email protected]
Malaysia is an upper middle income country of 27.5 million people located in the Asia Pacific
region. Over the past 50 years, the country has gradually established an extensive public
health system aimed at providing low cost comprehensive health care to all citizens based on
needs. The ultimate goal of the system is that of universal health coverage (UHC).
It is not possible to say when exactly UHC had been achieved in Malaysia. Earlier efforts to
gauge progress have been mainly restricted to compilation of general statistics on the numbers
and distribution of health facilities and skilled personnel. It is only in the last decade that
systematic work had been done to evaluate actual utilisation of services based on needs as well
as levels of financial risk protection afforded by the country’s health system. This paper will
present the results of work done thus far to assess UHC using multiple data sources from health
surveys, household expenditure surveys to administrative data as well as to highlight challenges
faced which are due mainly to issues with data quality and national representativeness.
Recent developments in the Malaysian health system have the potential to impact on some
measurement aspects of UHC. Due to budget constraints, the public health sector has been
unable to meet the expectations of the increasingly well informed health consumers. This,
combined with government efforts to promote Malaysia as a hub for health tourism in the region,
has led to the development of a thriving private for-profit health sector which is financed
predominantly from out-of-pocket payments. Whilst it is a longstanding perception that UHC in
Malaysia had been achieved mainly through the public health system, it is argued that the
current dual provider system necessitates a more holistic approach to continual evaluation of
UHC. This is all the more important in the context of the country’s multi-ethnic population which
exhibits differential preferences for public private care regardless of ability-to-pay. This paper
will thus also discuss the impact of health system changes on UHC as experienced by different
sub-populations in the country.
Key Words: Universal Health Coverage, Malaysian Health System, Public Private Care
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ISI-RSC 2014 ABSTRACT BOOK
IS30:
HIGH-DIMENSIONAL
COVARIANCE ESTIMATION
PENALIZED
REGRESSION
AND
Regression Shrinkage and Grouping of Highly Correlated Predictors with HORSES
Woncheol Jang
Seoul National University, Republic of Korea
[email protected]
Johan Lim
Seoul National University, Republic of Korea
johanlim at snu.ac.kr
Nicole A Lazar
University of Georgia, USA
[email protected]
Ji Meng Loh
New Jersey Institute of Technology, USA
[email protected]
Donghyun Yu
Keimyung University, Republic of Korea
Identifying homogeneous subgroups of variables can be challenging in high dimensional data
analysis with highly correlated predictors. The generalized fused lasso has been proposed to
simultaneously select correlated variables and identifies them as predictive clusters (grouping
property). In this article, we study properties of generalized fused lasso. First, we present a
geometric interpretation of the generalized fused lasso along with discussion of its persistency.
Second, we analytically show its grouping property. In third, we give comprehensive simulation
studies to compare our version of the generalized fused lasso with other existing methods and
show that the generalized fused lasso outperforms other variable selection methods in terms of
prediction error and parsimony. We describe two applications of our method in soil science and
near infrared spectroscopy studies. These examples having vastly different data types
demonstrate the flexibility of the methodology particularly for high-dimensional data.
Key Words: Generalized Fused Lasso, Persistency, Prediction Error, Parsimony
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ISI-RSC 2014 ABSTRACT BOOK
IS31: STATISTICAL METHODS AND APPLICATIONS IN HEALTH
SERVICES AND OUTCOME RESEARCH
Logic Regression for Provider Effects on Kidney Cancer Treatment Delivery
Mousumi Banerjee
Department of Biostatistics, University of Michigan, Ann Arbor, USA
[email protected]
In the delivery of medical and surgical care, often times complex interactions between patient,
physician, and hospital factors influence practice patterns. This paper presents a novel
application of logic regression in the context of kidney cancer treatment delivery. Logic
regression is an adaptive classification and regression procedure that is powerful as a data
analytic tool when the interaction between the predictors is of primary interest. Logic regression
searches for Boolean combinations of the original predictors that best explain the variability in
the outcome variable, and thus, reveals variables and interactions that are associated with the
response and/or have predictive capabilities. Using linked data from the US National Cancer
Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program and Medicare
we identified patients diagnosed with kidney cancer from 1995 through 2005. The primary
endpoints in the study were use of innovative treatment modalities, namely partial nephrectomy
and laparoscopy. Logic regression allowed us to uncover the interplay between patient,
provider, and practice environment variables, which would not be possible using standard
regression approaches. We found that surgeons who graduated on or prior to 1980 despite
having some academic affiliation; low volume surgeons in a non-NCI hospital; or surgeons in
rural environment were significantly less likely to use laparoscopy. Surgeons with major
academic affiliation and practising in HMO, hospital, or medical school based setting were
significantly more likely to use partial nephrectomy. Results from our study can inform efforts
towards dismantling the barriers to adoption of innovative treatment modalities, ultimately
improving the quality of care provided to patients with kidney cancer.
Key Words: Logic Regression, Interactions, Boolean Combination, Kidney Cancer, Treatment
Delivery.
Evaluation of the Accuracy of a Biomarker Adjusting for Covariate Effects for Outcome
Classification
Eunhee Kim
Brown University
[email protected]
Donglin Zeng
University of North Carolina at Chapel Hill
The Receiver Operating Characteristic (ROC) curve is a widely used measure to assess the
diagnostic accuracy of biomarkers for diseases. Biomarker tests can be affected by subject
characteristics, the experience of testers, or the environment in which tests are carried out, so it
is important to understand and determine the conditions for evaluating biomarkers. In this paper,
we focus on assessing the effects of covariates on the performance of the ROC curves.
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ISI-RSC 2014 ABSTRACT BOOK
In particular, we develop an accelerated ROC model by assuming that the effect of covariates
relates to rescaling a baseline ROC curve. The proposed model generalizes the accelerated
failure time model in the survival context to ROC analysis. An innovative method is developed to
construct estimation and inference for model parameters. The obtained parameter estimators
are shown to be asymptotically normal. We demonstrate the proposed method via a number of
simulation studies and apply it to analyze data from a prostate cancer study.
Key Words: Biomarker, Diseases, Prostate Cancer, Receiver Operating Characteristic
56
ISI-RSC 2014 ABSTRACT BOOK
IS32: ANALYSIS OF SURVEY DATA
On Invariant Post-Randomization for Statistical Disclosure Control
Tapan K. Nayak
Department of Statistics, George Washington University, Washington, DC 20052
[email protected]
Samson A. Adeshiyan
U.S. Energy Information Administration, Washington, DC 20585
In this paper, we investigate certain operational and inferential aspects of invariant PRAM (post
randomization method) as a tool for disclosure limitation of categorical data. Invariant PRAMs
preserve unbiasedness of certain estimators, but inflate their variances and distort other
attributes. We introduce the concept of strongly invariant PRAM, which does not affect data
utility or the properties of any statistical method. However, the procedure seems feasible in
limited situations. We review methods for constructing invariant PRAM matrices and prove that
a conditional approach, which can preserve the original data on any subset of variables, is an
invariant PRAM. For multinomial sampling, we derive expressions for variance inflation due to
invariant PRAMing and variances of certain estimators of the cell probabilities and also their
tight upper bounds. We discuss estimation of these quantities and thereby assessing statistical
efficiency loss due to invariant PRAMing. We find a connection between invariant PRAM and
creating partially synthetic data using a nonparametric approach, and compare estimation
variance under the two approaches. Finally, we discuss some aspects of invariant PRAM in a
general survey context.
Key Words: Categorical Data, Randomized Response, Sampling Design, Synthetic Data,
Unbiased Estimation, Variance Inflation
Identification Problem for the Analysis of Binary Data with Non-Ignorable Drop-Out
Kosuke Morikawa_
Osaka University, Osaka, Japan
[email protected]
Yutaka Kano
Osaka University, Osaka, Japan
[email protected]
When a missing-data mechanism is not missing at random(NMAR) or non-ignorable,
missingness is itself vital information and it must be taken into the likelihood, which, however,
needs to introduce additional parameters to be estimated. The incompleteness of the data and
introduction of more parameters can then cause the identification problem. When response
variables are binary, the problem becomes more serious because of less information of binary
data. If there are some instrumental variables, it is known that there exists a condition, under
which one can briefly verify whether a model is identified or not. When there are no instrumental
variables, there are no methods. In this talk, we provide a new necessary and sufficient
condition to easily check model identifiability when analyzing binary data with no instrumental
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ISI-RSC 2014 ABSTRACT BOOK
variables and non-ignorable missing by conditional models. We show that the condition can be
modified to be a simpler one so that one can easily check identifiability in practice.
Key Words: Incomplete Data, Dropout, Binary Data, Not Missing At Random, Identifiability
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ISI-RSC 2014 ABSTRACT BOOK
IS33: SOCIO-ECONOMIC INDICES IN COUNTRY SOCIETY
Where Are Peninsular Malaysia’s Most Deprived Areas?
Soo-Fen Fam
Department of Statistics, Malaysia
[email protected]
The country’s economy should be allocated optimally and effectively so that all levels of the
communities receive appropriate economic distribution. To ensure the achievement of this goal,
a suitable method that can be used to describe such efforts is needed. Among the measures
that can be undertaken by policymakers is to make decisions based on Deprivation Indices (DI).
The use of DI in the planning and allocation of resources have long been used by developed
countries such as United States, Canada, Europe, New Zealand and Australia. However, this
approach has not received attention in Malaysia. Therefore, this study develops DI by using
principal component analysis method (PCA) namely the General Index of Deprivation (GID) to
determine the socio-economic deprivation level of 81 administrative districts (ADs) in Peninsular
Malaysia. For this purpose, the Population and Housing Censuses in 1991, 2000 and 2010,
Vital Statistics, juvenile data and health data are utilized. The results of GID indicate that the
ADs can be ranked and classified into four quartiles; the most affluent, the moderately affluent,
the moderately deprived and the most deprived. Analysis by choropleth maps depict that the
majority of affluent areas were located in the west coast of Peninsular Malaysia whereas the
most deprived areas were mainly scattered in the northeast of Peninsular Malaysia for the three
censuses year respectively. These phenomena indicate the existence of patchiness and strong
spatial disparities of socioeconomic deprivation in several ADs. Thereby, GID denotes the
importance of considering geographic localization and spatial condition of each AD for allocating
resources and implementing efficient policies in Peninsular Malaysia.
Key Words: Deprivation index; Principal component analysis; Correlation
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ISI-RSC 2014 ABSTRACT BOOK
Socio-economic Development and Current Status on Quality of Life in Korea
Bongho Choi
Statistics Korea
[email protected]
Korea has experienced a profound change which was significantly reflected in every field of life
since 1960. Korea was predominantly an agrarian country characterized by intensive use of
human labor. The proportion of population aged 15 & over employed in agricultural sector has
rapidly decreased to 6.1% in 2013 from two thirds at the beginning of 1960s, meaning that
Korea is now one of urbanized and industrialized countries. Korea has also experienced rapid
and sustained economic growth since the 1960s. The per capita GNP rose from about 80
dollars in 1960 to and passed the 10,000-dollar mark in the mid-1990s, just before the country
faced an international financial crisis at the end of 1997. The per capita GNP plunged to 6,740
dollars in 1998, but recovered to the 10,000-dollar mark in 2000 and 20,000-dollar mark in 2006.
However, there is a saying that the quality of life of the Korean people has not been improved
corresponding to such rapid economic development.
Thus, this paper attempts to show 1) the socio-economic developments occurred over the last
several decades in Korea, 2) KOSTAT’s efforts to measure the quality of life, and 3) current
status on the quality of life of the Korean people.
Key Words: Socio-economic Development, Quality of Life
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ISI-RSC 2014 ABSTRACT BOOK
IS34:
APPLICATION
OF
STATISTICAL
TECHNIQUES
SURVEILLANCE AND DECISION/POLICY MAKING
FOR
Is Consumer Confidence Index useful in Forecasting Household Consumption in
Nigeria? Evidence from Survey Data
Olorunsola E. Olowofeso and Sani I. Doguwa
Statistics Department, Central Bank of Nigeria, Abuja
[email protected]
Based on data obtained from the regular Consumer Expectations Survey of the Central Bank of
Nigeria, this paper examines the usefulness of the Consumer Confidence Index (CCI) in
predicting household consumption in Nigeria. This study differs from past works on Nigeria as it
employs the Autoregressive Distributed Lag (ARDL) model, an appropriate methodology for
models that include both I(0) and I(1) variables. The estimated ARDL model allows us to gain
insight into the short and long run dynamics driving the relationship amongst the included
variables. Precisely, the work investigates whether incorporating consumer confidence and
retail trade indices into the consumption model improves its goodness of fit. The results are
quite revealing. First, the coefficients of two consumer sentiments variables, namely: consumer
confidence index and retail trade index are statistically significant. Second, the model with the
consumer sentiments variables provides the best fit to the data based on information criterion.
Third, the results of the out-of-sample forecast indicate that the model with the two consumer
sentiment variables outperformed the others as it led to an improvement of about 13.8 per cent
in the forecast performance of the baseline consumption model. Fourth, the error correction
coefficient is highest in the model with the consumer sentiment variables, implying that
consumer perceptions about the economy matter for the restoration of equilibrium following a
shock. This study concludes that the consumer confidence index is a critical determinant of
consumer spending in Nigeria.
Key Words: Consumer Confidence Index, Forecast, Household Consumption, Survey Data
The Use of Data Reduction Techniques to Assess Systemic Risk: An Application To The
Chilean Banking System
Diego Avanzini
Central Bank of Chile
[email protected]
Alejandro Jaray
Central Bank of Chile
In recent years, data reduction techniques have gained scene in applied economics. Following
this trail, we apply and extend the use of a data reduction technique such as principal
component analysis (PCA), to assess the extent of systemic risk. In this paper, we use related
tools to study three related aspects of the Chilean banking systemic risk: (i) to what extent the
degree of common risk exposure has changed over the past decades; (ii) during which periods
this exposure increased the most; and (iii) based on prede…ned thresholds, when this
commonality has become system-ically relevant. Additionally, we identify systemically important
nancial institutions (SIFIs) based on their contribution to the degree of common risk exposure
during periods of higher systemic risk. We …nd that prior to the 2008 � 09 global nancial crisis
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ISI-RSC 2014 ABSTRACT BOOK
the degree of common risk exposure in Chile increased signicantly, and that the banks that
contributed the most were not necessarily the biggest ones in size, as measured by their assets
share.
Key
Words:
Common
Factors,
Systemic
Risk,
Principal
Component
Analysis.
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ISI-RSC 2014 ABSTRACT BOOK
IS35: APPLICATION OF STATISTICS IN CREDIT RISK MANAGEMENT
Why Banks Fail: A Forward Intensity Model for Default and Distressed Exits
Oliver Chen
Risk Management Institute, National University of Singapore, 21 Heng Mui Keng Terrace,
Singapore 119613
[email protected] or [email protected]
Elisabeth Van Laere
Risk Management Institute, National University of Singapore, 21 Heng Mui Keng Terrace,
Singapore 119613
[email protected]
In this paper we present a new early warning model for bank default. An important but
previously little studied issue is the fact that banks in distress might exit through, for example,
an acquisition or nationalization rather than a default. To distinguish between these different exit
paths, we extend the forward intensity model by introducing a triply stochastic process. We
show that including distressed exits as a separate path increases the prediction accuracy of
bank default prediction, in addition to producing an additional credit risk benchmark. Without
imposing dynamics on the covariates, our model predicts defaults and distressed exits up to 5
years in the future. This paper shows that all previous empirical studies on bank failure that
ignore bank distressed exits are incomplete. Accounting for both default and distressed exit
results in a more comprehensive and more accurate credit risk measure. The potential spill-over
effects and damage of bank failure to different parts of the economy make this paper highly
relevant in the current macro-economic environment. Furthermore, this study can aid in
improving market discipline and help bank supervisors to manage a healthier banking system.
Key Words: Bank Default, Bank Distress, Probability of Default, Financial Crisis, Financial
Distress, Forward Intensity
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ISI-RSC 2014 ABSTRACT BOOK
An Application of Techniques for Improving Model Rank Accuracy under Various Data
Conditions
Yoon-Tien Yap
RHB Banking Group, Kuala Lumpur, Malaysia
[email protected]
We consider risk modelling with a focus on improving out-of-sample rank accuracy under a
variety of data conditions. We illustrate the benefits of bootstrap aggregation when the data is
unstable. The benefits of non-parametric methods are quantified under various target-covariate
relationship complexities. We examine the alternatives to linear regression when the distribution
of the target is bimodal. Benchmark performance levels are based on parametric methods. The
models discussed in this study will potentially involve all three Basel II risk parameters as
required to present the concepts. Ultimately the objective of the study is to illustrate the types of
approaches that a modeller can consider when improvement of rank accuracy is the goal.
Key Words: Basel II Risk Parameters, Bootstrap Aggregation, Non-Parametric Methods,
Unstable Data, Relationship Complexity, Bimodal Target
Prediction of the insolvent borrowers in Egypt
Nany Abd El-kader
CAPMAS, Cairo, Egypt,
[email protected]
The Public Egyptian Commercial Banks (PECBs) play an important role in the economic activity.
In recent decades, given the developing economic conditions, the state facilitates loans for the
youth in different activities. These loans are known as loans of small projects supported by
Social Fund for Development and related to PECBs. Generally, the insolvency phenomenon has
a negative effect on the economic activity. This paper investigates the power of classification to
predict the insolvent borrowers by using three methods. These methods are logistic regression,
latent class regression analysis method and classification and regression trees. The data are
collected from PECDs which are Elahli, Misr, Cairo and Alexandria. The total actually sample
size will be used is 1047 cases, divided into 90% used as sample analysis and 10% as
prediction sample. Results show that the correct percentages of insolvent estimation were
16.2%, 35.66% and 65.4% by logistic regression, latent class regression analysis and
classification and regression trees respectively.
Key Words: Classification and Regression Tree, Expectation Maximization Algorithm, Latent
Class Regression Analysis
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ISI-RSC 2014 ABSTRACT BOOK
IS36: STATISTICS IN ACTION: TEACHING, RESEARCH, AND
CONSULTING
Practical Experiences for Modeling Work as Statistical Collaborators and Consultants
John Bailer
Dept. of Statistics, Miami University, Oxford, Ohio 45056, USA
[email protected]\
A report summarizing responses of recent statistics graduates and employers of these
graduates described skills associated with the most successful recent graduates. These skills
included a solid foundation in statistical theory and methods, programming, communication
skills, collaboration, teamwork, and leadership. Related experiences that were highly valued
included working with non-routine, real problems throughout their education and internshIS, coops or other significant immersive work experiences. This report will be summarized, and then
two courses that implement recommendations are discussed. The first course is a data
practicum course that requires students to work on real problems for real clients in teams and
then report the results of their analysis to clients. The second course is a data visualization
course that is similar in that teams work for real clients; however, this course adds the
dimension that teams involve multiple disciplines with a diversity of student technical
backgrounds and expertise. The details associated with conducting these courses will be
discussed, and suggestions for implementing courses of this type will be presented.
Key Words: Graduates, Skills
Education For A Workplace Statistician
Helen MacGillivray
Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia
[email protected]
Transitioning from university student to a statistician working as a collaborative researcher,
consultant and workplace educator as well as a data scientist is a very daunting and challenging
task. Statistical workplaces can involve at least some aspects of all these components, and
some will more easily facilitate learning on the job than others. But all workplace statisticians
need proficient statistical thinking, problem-solving and communication skills, as well as a sound
statistics foundation for ongoing learning to competently understand, perform and possibly
develop statistical analyses. This presentation includes a review of decades of literature from
statisticians on how to educate for, and be, a good statistical consultant, and demonstrates the
synergies with, and implications for, progress in statistical education. Based on feedback from
statistical graduates, we then discuss why and how early authentic experiential and constructive
learning in statistical data analysis and problem-solving courses, combined with experience
gained in a developmental and mentored program in tutoring such courses, can build the key
skills for a workplace statistician. A brief discussion of similarities and comparisons between
workplace and consultant statisticians concludes with some summary recommendations.
Key Words: Statisticians, workplace, Consultants, Learning
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ISI-RSC 2014 ABSTRACT BOOK
Negotiating the Path of Academia: Challenges for Statisticians In Developing Countries
Shyamala Nagaraj
University of Michigan, Ann Arbor USA
Formerly with University of Malaya, Kuala Lumpur, Malaysia
[email protected]
The path of academia can be a long, lonely and challenging one, as the young PhD negotiates
through the academic system of teaching, research, aiming for recognition and reward from
stellar evaluations, substantive grants and highly-cited publications. Along the way, the
academic hopes ultimately to contribute, even just a sliver, to knowledge gain in the field in
which he or she spent four, five or even eight years completing a doctoral dissertation. This talk
highlights the issues and challenges faced by an academic in a developing country and also the
opportunities. The academic will have to contend with making choices that may not always lead
to working in the chosen field, balancing between the need to publish in international journals,
and the opportunities available for research.
Key Words: Academic, Research
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ISI-RSC 2014 ABSTRACT BOOK
IS37: NON-PARANETRIC AND SEMI-PARAMETRIC INFERENCE
In-Sample Density Forecasting
Byeong U. Park
Seoul National University
[email protected]
Young K. Lee
Kangwon National University
[email protected]
Enno Mammen
Universitat Mannheim
[email protected]
Jens P. Nielsen
Cass Business School, City University
[email protected]
This paper generalizes recent proposals of density forecasting models and it develops theory for
this class of models. In density forecasting the density of observations is estimated in regions
where the density is not observed. Identification of the density in such regions is guaranteed by
structural assumptions on the density that allows exact extrapolation. In this paper the structural
assumption is made that the density is a product of one-dimensional functions. The theory is
quite general in assuming the shape of the region where the density is observed. Such models
naturally arise when the time point of an observation can be written as the sum of two terms
(e.g. onset and incubation period of a disease). The developed theory also allows for a
multiplicative factor of seasonal effects. Seasonal effects are present in many actuarial, biostatistical, econometric and statistical studies. Smoothing estimators are proposed that are
based on back-fitting. Full asymptotic theory is derived for them. A practical example from the
insurance business is given producing a within year budget of reported insurance claims. A
small sample study supports the theoretical results.
Key Words: Density Estimation, Kernel Smoothing, Back-fitting, Chain Ladder
Nonparametric Analysis of Covariance in Partial Linear Models with Factor-by-Curve
Interactions
Li-Shan Huang
Institute of Statistics, National Tsing Hua University, Taiwan
[email protected]
We develop nonparametric analysis of covariance tools for a partial linear model that include a
factor-by-curve interaction. In other words, the model allows for possible nonlinear covariate
effects which can have different shapes in different factor levels. Semiparametric F-tests are
proposed to testing the significance of the factor-by-curve interaction, i.e., testing equality or
parallelism of multiple regression curves. We demonstrate the testing procedures and their
properties over simulated data. This is a joint work with Weiqiao Hong and Wenhsiung Nien.
Key Words: Local Polynomial Regression, Smoother Matrix, ANOVA Decomposition
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ISI-RSC 2014 ABSTRACT BOOK
Semiparametric Isotonic Regression
Kyusang Yu
Konkuk University, South Korea
[email protected]
In this talk we discuss non- and semi-parametric additive isotonic regression models. We
discuss a backfiiting estimator and the efficiency bound of the model and the least squares
estimator under this model. We show that the ordinary least square estimator studied by Huang
(2002) and Cheng (2009) for the semiparametric isotonic regression achieves the efficiency
bound for the regular estimator when the true parameter belongs to the interior of the parameter
space. We also show that the result by Cheng (2009) can be generalized to the case that the
covariates are dependent on each other.
Key Words: Semiparametric Isotonic Regression, Backfiiting Estimator, Efficiency Bound
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IS38: STATISICAL METHODS IN PUBLIC HEALTH
Estimation of Antibody Concentration by Weighted Average of Multiple Dilution Data
Yin Bun Cheung*
Duke-NUS Graduate Medical School, Singapore
[email protected]
Ying Xu
Duke-NUS Graduate Medical School, Singapore
[email protected]
In medical research, measurement of concentrations of substances usually involves using a 4parameter calibration curve to map the observed responses to the underlying values. The
Enzyme-linked ImmunoSorbent Assay (ELISA), which is commonly used to measure antibody
concentration, is an example of such measurements. This and similar assay methods have a
limitation that an accurate measurement is obtainable only if the observed response falls within
the optimal, near-linear range of the calibration curve. Typically, a series of four dilutions of the
samples is assayed, so that at least some of the diluted samples are within the optimal range.
This results in four sets of observed responses. Practices vary, but it is quite common to
somehow define a single set of responses as optimal and use it for statistical analysis. This
common practice is wasteful and reduces accuracy. We propose a weighted average estimator
and two simplified forms of it for fully utilizing the information from multiple dilutions. The
estimator uses weights inversely proportional to the variances of the dilution-specific calibrated
values. The weights reflect the observations’ positions in relation to the optimal region of the
calibration curve, unequal variances, and uncertainty in calibration curve parameters. The
simplified forms assume away unequal variances and/or uncertainty in the calibration curve
parameters. The simplified forms are very easy to use by laboratory researchers and
substantially superior to conventional practices and only slight inferior to the full version in terms
of root mean square error. We evaluate the proposed method and compare with conventional
methods by simulation. An experimental study of malaria vaccine candidates, with ELISA
measurement of antibody concentration, is used to illustrate.
Key Words: Calibration Model, Laboratory Assays, Weighted Average Estimation, Vaccine
Trials
Adjusting Reporting Bias in Passive Pharmacovigilance
Dr. Palash Ghosh
Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore 169856
[email protected]
World Health Organization (WHO) defines pharmacovigilance as the science and activities
relating to the detection, assessment, understanding and prevention of adverse effects or any
other drug related problems. In practice, pharmacovigilance almost exclusively refers to the
spontaneous reporting systems which allow health care professionals and others to report
adverse drug reactions to a central agency. It is well-known that a spontaneous reporting (SR)
system suffers from significant under-reporting of adverse drug reaction (ADR) from the source
population. The existing methods do not adjust for such under-reporting for the calculation of
measures of association between a drug and the ADR under study. Often there may be direct or
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ISI-RSC 2014 ABSTRACT BOOK
indirect information on the reporting probabilities. In this work, we discuss the existing
methodologies and their drawbacks. By combining external information with SR data and using
case-control structure of the SR data, the proposed methodology estimates the association
measure unbiasedly. The trade-off between the amount of external information required and the
assumptions made on SR data is also discussed. Using multi-sample structure of the data, we
obtained the asymptotic distribution of estimated model parameters. We consider data from
adverse event reporting system (AERS), Food and Drug Administration (FDA), US, to apply the
developed methodologies.
Key Words: Pharmacovigilance, Adverse Drug Reaction, Adverse Event Reporting System
Sorting Multiple Classes
Nonparametric Approaches
in
Multi-dimensional
ROC
Analysis:
Parametric
and
Jialiang Li
Department of Statistics & Applied Probability, National University of Singapore
[email protected]
In large scale data analysis, such as in a microarray study to identify the most differentially
expressed genes, diagnostic tests are frequently used to classify and predict subjects into their
different categories. Frequently, these categories do not have an intrinsic natural order even
though the quantitative test results have a relative order. Because identifying the correct order
for a proper definition of accuracy measures is important for a high dimensional ROC analysis,
we propose rigorous and automated approaches to sort out the multiple categories using simple
summary statistics such as means and relative effects. We discuss the hypervolume under the
ROC Manifold (HUM), its dependence on the order of the test results and the minimum
acceptable HUM values in a general multi-category classification problem. Using a leukemia
data set and a liver cancer data set, we show how our approaches provide accurate screening
results when we have a large number of tests.
Key Words: ROC Analysis, Diagnostic Tests
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ISI-RSC 2014 ABSTRACT BOOK
IS39: CLASSIFICATION AND CLUSTERING WITH APPLICATIONS
Quadratic Discriminant Classifier for Moment-Based Face Recognition
Tamanna Howlader*
Institute Statistical Research and Training, University of Dhaka, Dhaka-1000, Bangladesh
Email: [email protected]
Syed Shahnewaaz Ali
Institute Statistical Research and Training, University of Dhaka, Dhaka-1000, Bangladesh
E-mail: [email protected]
S. M. Mahbubur Rahman
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and
Technology, Dhaka-1205, Bangladesh
Email: [email protected]
The quadratic discriminant classifier (QDC) is a well-known parametric Bayesian classifier that
has been successfully applied to various statistical pattern recognition problems. Notably, the
use of such classifier may be constrained in applications, where the variance-covariance matrix
of the high dimensional feature vector in a class needs to be estimated from a very small
sample. In emerging biometric security applications, the automated two dimensional (2D) face
recognition scheme very often possesses a number of training samples per class that is much
less than the length of the feature vector per face image. The sample covariance matrix is a
poor estimator in these situations, and hence such estimator causes the QDC to become
unstable. This paper presents a simple remedial measure in which the class-specific shrinkage
estimates of the variance covariance matrix are combined to obtain a pooled shrinkage estimate
that gives greater stability to the QDC in small sample situations and, in particular, for the face
recognition problem. The proposed face recognition method uses the 2D orthogonal moments
as features with a view to preserve the local information of images that may be useful for face
recognition. Experiments performed on commonly-referred databases demonstrate remarkable
improvements in the recognition rate of the QDC over that of the Naive-Bayes classifier and the
well-established principal component analysis or linear discriminant analysis-based methods,
when ratios between the length of feature vector and number of training sample is quite high.
Key Words: Intraclass Correlation Coefficient, Orthogonal Moments, Quadratic Discriminant
Classifier, Shrinkage Estimator, Statistical Pattern Recognition
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ISI-RSC 2014 ABSTRACT BOOK
Global Metric Learning for Nearest Neighbor Classification
Akarin Phaibulpanich
Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University,
Bangkok, Thailand
[email protected]
Kerby Shedden
Department of Statistics, University of Michigan, Ann Arbor, USA
[email protected]
Methods for constructing an 𝐴-weighted metric (𝑥−𝑦)′𝐴(𝑥−𝑦) that improves the performance of
𝐾-nearest neighbor (KNN) classifiers are developed. KNN is known to be highly flexible, but can
be somewhat inefficient and unstable. By incorporating a parametrically optimized metric into
KNN, global dimension reduction is carried out efficiently, leaving the most difficult nonlinear
features of the problem to be solved on a low dimensional projected feature space. Optimization
over 𝐴 is done by formulating a probability model that captures KNN’s essential property – using
only a local neighborhood of training cases to predict the class of a test case. The expected
correct vote margin can be calculated under the probability model and optimized over 𝐴 using
gradient methods to yield a metric that is adapted to a particular problem. This framework
incorporates variable selection as well as variate selection, in which certain linear combinations
of the variables are deemed either informative or completely uninformative. The estimated 𝐴
matrix can be used for both classification and data analysis, as it contains information about
which features are informative (either in a linear or nonlinear sense), or completely
uninformative about class membership.
Key Words: 𝐴-Weighted Metric Learning, Global Dimension Reduction, K-Nearest Neighbor
Classification
Pattern Classification for Earth Surface Temperature Changes in the Arctic
Wandee Wanishsakpong*
Prince of Songkla University, Pattani, Thailand
[email protected]
Nittaya Mcneil
Prince of Songkla University, Pattani, Thailand
[email protected]
The trends and patterns of monthly seasonally adjusted temperatures in the arctic zone were
examined using statistical methods. The data were obtained from temperatures recorded
between 1973 and 2013 from the climate research unit. The data was filtered with a second
order autoregressive process to remove autocorrelations. Factor analysis was used to classify
regions with similar temperature changes and identified twelve factors. The temperature in the
twelve factors showed a significant increase. Large temperature increases (at least 0.19oc) were
found in the North Siberia, South Siberia and part of the Arctic Ocean. Increases in North
Canada, Alaska, North Pacific Ocean, East Siberia, Iceland, Norway and Sweden were
moderate (0.15oc to 0.16oc). The North East Canada, Greenland and its surrounding North
Atlantic Ocean and Arctic Ocean had increases below 0.15oc.
Key Words: Time Series Analysis, Autocorrelation, Linear Regression Model, Factor Analysis
72
ISI-RSC 2014 ABSTRACT BOOK
IS41: HOUSING BUBBLE: MEASUREMENT OF HOUSING PRICE
Issues related to House Price Statistics – Indian Experience
Sangeetha Mathews, Ravi Shankar and Pavan Jindam
Reserve Bank of India
[email protected]
Real estate activity is a key engine of economic growth which has assumed considerable
significance over the years due to its inter-linkage with macroeconomic stability. In the emerging
market economies, housing sector activities have implications for basic needs as well as wealth
effect and speculation, which necessitate tracking housing prices for macro policy purposes. In
India, house ownership is critical for very large population to meet general aspirations of
economic well-being. Absence of transparent market, however, makes it imperative to tap
alternative sources of housing price data. Against this background, we discuss alternate data
sources for assessing movements of house price in India. We discuss in detail the Reserve
Bank’s approach to initiate a ‘Residential Asset Price Monitoring Survey’ based on transactionlevel housing loan data from banks and housing finance companies. This intends to provide
effective information system for use in monetary and financial stability policy formulation. The
evolution and progress of the survey, the compilation of house price indices and other
measures, methodological / statistical issues in compiling such indices are discussed.
Key Words: Asset Prices, House Price Index, Monetary Policy, RPPI
Liquidity Shocks and the US Housing Crisis of 2007-08
Gianni La Cava
Reserve Bank of Australia
[email protected]
Anecdotal evidence suggests that a signicant tightening in credit conditions occurred in the
United States following the collapse of the loan securitisation market in 2007. We examine
whether the fall in mortgage credit over 2007-2008 was caused by a reduction in credit supply
which, in turn, can be traced to a fall in the amount of nancing available to mortgage lenders.
Using loan-level information, we show that mortgage lenders that were particularly reliant on
securitisation disproportionately reduced the supply of mortgage credit. The negative liquidity
shock caused by the shutdown of the securitisation market explains a signicant share of the
aggregate decline in mortgage credit during the crisis.
Key Words: Housing; Mortgage Credit; Securitisation; Subprime Crisis
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ISI-RSC 2014 ABSTRACT BOOK
Monitoring House Prices from a Financial Stability Perspective – the BIS Experience
Bruno Tissot
Bank for International Settlements
[email protected]
Reflecting the importance of these indicators for dealing with financial stability issues, the BIS
disseminates statistics on property prices for a wide range of countries. This data collection has
been reinforced since the 2007/08 financial crisis, not least in the context of the data gaps
initiative endorsed by the G-20. Moreover a growing attention is being paid to the long-term
trends in property prices, which are an important element in determining the evolution of
financial cycles across countries.
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ISI-RSC 2014 ABSTRACT BOOK
IS43: FINANCIAL INCLUSION MEASUREMENT
Financial Capability of Malaysians: Evidence from OECD (INFE) 2010 Data
Yiing Jia LOKE
School of Social Sciences, Universiti Sains Malaysia, 11800 Penang, Malaysia
[email protected]
Financial capability is a term used to reflect an individual’s ability to manage financial resources
competently and responsibly. Using the pilot survey data from OECD (International Network for
Financial Education, INFE) 2010, financial capability in this paper is measured based on four
types of financial behaviour: whether the individual plans a budget, lives within their means, is
prepared for income shock and owns an insurance policy. The measurement of financial
capability is divided into five levels depending on the number of financial behaviours that an
individual displays. Given the categorical and clear ordering of the financial capability
measurement, ordered probit is used to determine socio-economic factors that are significant in
explaining the varying differences in the levels of financial capability among Malaysians. The
paper also identifies the financial knowledge gaps and investigates the levels of financial
knowledge of Malaysians. While the majority of Malaysians show an average level of financial
knowledge and plan their budget, many are financially unprepared for future needs and
unexpected circumstances. The findings from the ordered probit shows that socio-economic
factors and financial knowledge can significantly explain the varying differences in the levels of
financial capability among Malaysians. In particular, the findings show that ethnicity, income,
gender, regularity of income, education, age and household size have significant effect on
individuals’ financial capabilities. The findings have implications for regulators, financial
educators and consumer groups in their efforts to enhance individuals’ financial capabilities.
Key Words: Emergency Saving, Financial Behaviour, Financial Knowledge, Living within One's
Means, Personal Finance
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ISI-RSC 2014 ABSTRACT BOOK
IS44: INTERNATIONAL TRADE AND FINANCE
Analyzing Price Discovery Function of Crude Palm Oil Futures (FCPO) Before and After
Shari’ah Compliant
Noryati Ahmad
Arshad Ayub Graduate Business School, Universiti Teknologi MARA, Shah Alam, 40450
Malaysia
[email protected]
This paper investigates the price discovery function of Malaysian crude palm oil futures (FCPO)
before and after Shari’ah compliance. The sample used in the study comprises of crude palm oil
futures (FCPO) and crude palm oil (CPO) prices for the period January 2007 until December
2011. The period is divided into two sub periods: Period I (January 2007 – July 2009) before
shari’ah compliant and Period II (August 2009 – December 2011) after shari’ah compliance.
Results of Augmented Dickey Fuller (ADF) and PhilIS Perrons (PP) unit roots tests suggest that
the CPO and FCPO series are integrated at first difference. Johansen’s co-integration test
indicates that FCPO and its underlying spot market (crude palm oil) for both periods are cointegrated, implying that there is a casual relationship between the two markets. Estimated
results of Vector Error Correction model (VECM) during period I indicate one-way causality
direction from CPO market to FCPO market. This can be interpreted as price discovery process
that occurs from its underlying spot market to FCPO market. By contrast, price discovery
function of the crude palm oil futures market becomes increasingly more prominent after it is
being classified as shari’ah-compliant.
Key Words: Crude Palm Oil Futures, Shari’ah Compliance, Price Discovery
Trade Competitiveness Determinants in Emerging and Developed Countries
Mazila Md-Yusuf*
Arshad Ayub Graduate Business
[email protected]
School,
Univeristi
Teknologi
MARA,
Malaysia,
Amirhossein Karbalaei
Arshad Ayub Graduate Business School, Univeristi Teknologi MARA, Malaysia
Catherine S F Ho
Arshad Ayub Graduate Business School, Univeristi Teknologi MARA, Malaysia
Trade flow is an essential stimulus to facilitate economic development of emerging countries.
Some of the major challenges for many countries around the globe are to promote international
trade and competitiveness as well as optimize economic policies in order to increase trade
flows. The aim of this paper is to investigate the effects of macroeconomic and country specific
indicators on trade flows in fast emerging BRICS countries, Asian developing countries and
developed countries. This study employs cross-sectional fixed effect panel data analysis over
the period 1981-2010 to reveal significant empirical evidence. Findings confirmed that trade
flows in fast emerging BRICS countries are modeled as a function of exchange rate, economic
growth, unemployment rate and government budget balance.
The important drivers of trade flows in the Asian developing countries are economic growth,
interest rate and government budget balance. The trade flows in developed countries are
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ISI-RSC 2014 ABSTRACT BOOK
determined by economic growth, inflation rate, interest rate, government budget balance, wage
rate and stock market performance. Comprehensive results in this study provide evidence that
macroeconomic variables and country specific indicators are significant determinants of trade
flows.
Key Words: BRICS, Developed Countries, Trade Flows, Macroeconomic Fundamentals
The Impact of Non-Tariff Barriers on the Import of Agricultural Products in Malaysia
Azlina Hanif
Universiti Teknologi MARA
[email protected]
Despite the calls by the WTO to reduce or totally eliminate non-tariff barriers (NTBs), the
incidence of NTBs is still pervasive. In Malaysia, NTBs are profound in the agriculture sector.
Thus, the issue that the paper seeks to address is whether the NTBs are actually import
reducing. In doing so, the level of NTBs in the agriculture sector is first quantified. The ARDL
technique to cointegration is then employed to determine the relationship between aggregate
import and NTBs in Malaysia’s agriculture sector as well as other control variables such as
tariffs, relative price and income during the 1978 to 2012 period. In addition, we examine the
effect of NTBs on disaggregated imports such as poultry and wheat flour. The findings from the
study are expected to confirm that NTBs do affect agricultural import either at the aggregate or
disaggregated level.
Key Words: Non-Tariff Barriers, Agriculture
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ISI-RSC 2014 ABSTRACT BOOK
IS45: EXTREME VALUE THEORY
Probabilistic Analysis of Extreme Rainfall In Malaysia
Zalina Mohd Daud
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia,
Jln. Semarak, Kuala Lumpur
[email protected]
Syafrina Abdul Halim
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia,
Jln. Semarak, Kuala Lumpur
Norzaida Abas
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia,
Jln. Semarak, Kuala Lumpur
Norazizi Mohamed
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia,
Jln. Semarak, Kuala Lumpur
The occurrence of rainfalls of high magnitude constitutes a primary natural hazard in many
countries including Malaysia. Studies in relation to extreme rainfall analysis have received
numerous attention, in particular the fitting of probability distributions and parameter estimations
associated with it. Series investigated include annual maximum, peak over threshold, partial
duration and even inter event time duration series of hourly, daily, monthly and annual
resolution. An overview of such studies done in Malaysia and a discussion of the results
concluded will be presented. Next this paper discusses the historical trend in hourly extreme
rainfall from the years 1975 to 2010, focusing on eight extreme rainfall indices. The 95th and
99th percentiles were selected as the threshold level and results show that both extreme
frequency and intensity indices contribute to positive signs in trend. The trend analysis was
conducted using linear regression and kriging was used to determine the spatial pattern of
trends in seasonal extremes. A stochastic downscaling Advanced Weather Generator method
composed of the Neyman-Scott Rectangular Pulse and an Autoregressive Lag-1 model was
employed using a multi-model ensemble approach to produce future projections of hourly
extreme precipitation. This gives an insight into the future behaviour of extreme events in
Malaysia up to return periods of 10-40 years. The extreme precipitation for both hourly and 24-h
seems to increase in future while extreme of wet spells will remain unchanged.
Key Words: Advanced Weather Generator, Trend Analysis, Stochastic Downscaling
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ISI-RSC 2014 ABSTRACT BOOK
Extreme Value Modelling Using Hourly Rainfall Series in Klang Valley
Wendy Ling Shinyie
School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan
Malaysia, 43600 Bangi Selangor, Malaysia
[email protected]
Noriszura Ismail
School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan
Malaysia, 43600 Bangi Selangor, Malaysia
Wan Zawiah Wan Zin
School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan
Malaysia, 43600 Bangi Selangor, Malaysia
Abdul Aziz Jemain
School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan
Malaysia, 43600 Bangi Selangor, Malaysia
Analysis of extreme events is currently one of the leading research topics in the field of
climatology due to the potentially dangerous phenomena associated with extreme events. One
of the main approaches in Extreme Value Theory is annual maximum series (AMS), however,
this series requires large dataset and is wasteful of data. As an alternative to the AMS, the r
largest observations method and partial duration series (PDS) method have been advocated. In
this study, these two alternative methods are implemented to study extreme hourly rainfall data
in Klang Valley. Adapted Hill estimator is utilized to select the optimal threshold for PDS
method. Four distributions namely Generalized Extreme Value distribution, Generalized Pareto
distribution, Generalized Logistic distribution and Generalized Normal distribution are fitted to
the two models. The performance of the two models in terms of uncertainty of the T-year event
estimator is evaluated by L-moments estimation method.
Key Words: r Largest Observations, Partial Duration Series, L-Moments
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ISI-RSC 2014 ABSTRACT BOOK
IS46: MODELLING AND ANALYSIS WITH APPLICATIONS – I
The Fuzzified Employability Model for the Perceived Multiple Intelligence of People with
Epilepsy
Siti Rahmah Awang*
Faculty of Management and Human Resource Development, Universiti Teknologi Malaysia,
81310 Skudai, Johor
[email protected]
Rasimah Aripin
Department of Statistics, FTMSK, Universiti Teknologi MARA, 40450 Shah Alam
Md. Hanip Rafia
Department of Neurology, General Hospital Kuala Lumpur, 50586 Kuala Lumpur
[email protected]
Tahir Ahmad
Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor
[email protected]
The focus of this paper is on the development of an employability model for PWE. It describes
the use of logistic regression to produce a crisp model. Next, fuzzy procedures are integrated
into the crisp model to come up with a fuzzy model, which would be used to identify the optimal
parameters of the eight intelligence skills. Then, the algorithms of the model were coded into C
Sharp (C#) programming language.
Key Words: People with Epilepsy, Fuzzy Model, Multiple Intelligence Theory
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ISI-RSC 2014 ABSTRACT BOOK
Statistical Analysis of Discrete Data with COM- Poisson and Other Families of
Distributions
S. H. Ong
Institute of Mathematical Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia
[email protected]
S. Z. Sim
In this presentation, we survey and further examine the Conway–Maxwell (COM) Poisson
distribution which has attracted the attention of many researchers.The COM-Poisson distribution
has one more parameter than the Poisson distribution which gives it the flexibility to cater for
under-, equi- and over-dispersion in count data. Other distributions with this flexibility are also
discussed. The COM-Poisson distribution is compared with the Poisson distribution with respect
to some stochastic orderings. Computational issues regarding the evaluation of the normalizing
are considered. Likelihood ratio test and the score test are developed to test the importance of
the additional parameter that controls the type of dispersion. The performance of the two tests is
examined through Monte Carlo simulation studies. Application to real data sets are also
presented.
Key Words: Distribution, COM-Poisson
Estimation and Forecasting with Logarithmic Autoregressive Conditional Duration
Models: A Comparative Study with an Application
Ng, Kok Haur
Institute of Mathematical Sciences, University of Malaya
[email protected]
This talk presents an alternative method for estimation of parameters in logarithmic
autoregressive conditional duration (Log-ACD) models based on the theory of Estimating
Functions (EF). Theoretical results for the EF estimates are derived and a simulation study is
conducted to compare the performance of the EF estimates with the corresponding ML
(maximum likelihood) and QML (quasi maximum likelihood) estimates. It is argued that the EF
estimates are easier to evaluate than the ML, can be obtained faster than the ML and have
sampling properties comparable with those of the ML and QML methods. Finally, we apply all
three methods to a real financial duration dataset and compare various ACD model types and
the estimators. Results show that the Log-ACD (1, 1) models provide relatively smaller variation
in forecast errors than Linear ACD (1,1) models regardless of estimation method used. In
addition, we apply the Diebold-Mariano (DM) and superior predictive ability (SPA) tests to show
that there were no significant differences in forecast ability for all models and methods.
Key Words: Estimating Functions, Maximum Likelihood, Quasi Maximum Likelihood
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ISI-RSC 2014 ABSTRACT BOOK
IS47: STATISTICS IN PUBLIC HEALTH – II
Estimation of Vaccination Coverage Rates by Combining Time Series and Cross
Sectional Data
Santanu Pramanik
Public Health Foundation of India, India
[email protected]
Information on population health indicators in India come from a number of surveys that vary in
periodicity and depth. For instance, the most recent data on immunization and vaccination
coverage indicators are derived from the first round of Annual Health Survey (AHS-1, 2010-11),
but these were conducted only in nine high focus states of India. The latest national surveys of
immunization coverage were conducted in 2009 (Coverage Evaluation Survey). Therefore,
reliable immunization coverage data for the entire country since 2009 is lacking. We use an
established approach of small area estimation to predict coverage rates of several vaccinations
for the remaining states and union territories (not covered by AHS-1) in 2011. We used a linear
mixed model that combines data from five cross sectional surveys representing five different
time points. Our model encompasses sampling error of the survey estimates, area specific
random effects, auto-correlated area by time random effects and borrows strength across areas
and time points. Model-based estimates are almost identical to the AHS-1 estimates for the nine
states in 2011, suggesting that our model provides a realistic representation as AHS-1
estimates are highly precise because of their large sample size. Results indicate that coverage
inequality between rural and urban areas has been reduced significantly for most states in India.
The National Rural Health Mission has had both supply side and demand side effects on the
immunization program in rural India. In combination, these effects explain the narrowing of
immunization performance between urban and rural areas.
Key Words: Vaccination, Immunization, Health
A Bayesian Analysis for Detecting the Waning of Vaccine Effectiveness
Jin Kyung Park*
International Vaccine Institute, Korea
[email protected]
Joungyoun Kim
Samsung hospital, Korea
[email protected]
Thomas F Wierzba
International Vaccine Institute, Korea
[email protected]
Yongdai Kim
Seoul National University, Korea
[email protected]
Vaccine effectiveness is the ability of vaccines to prevent outcomes of interest in the real world.
It is assessed from the follow-up surveillance study at population-level rather than individual82
ISI-RSC 2014 ABSTRACT BOOK
level. In a cohort study, the Cox's proportional hazards model is used to estimate the vaccine
effectiveness defined as one minus hazards ratio. The main assumption of the model is
proportionality, that is, the effectiveness in the fields maintains statistically constant vaccine
effectiveness over the study period. The proportionality is assessed descriptively or by using the
rescaled Schoenfeld residuals through the simulation. In a vaccine trial, it is important to figure
out the vaccine waning during the time of development of vaccination strategy to optimize the
impact of vaccine as well as cost effectiveness, especially, when new vaccine is introduced. We
proposed the Bayesian approach to estimate the vaccine effectiveness and test whether the
waning of vaccine effectiveness exists. The proposed method identifies the time point of waning
if it exists.
Key Words: Vaccine Effectiveness, Bayesian, Proportional Hazards Model
Determinants of the Socioeconomics and Spatial Pattern of Malnutrition in India: A
Geoaddative Semi-Parametric Regression Approach
Awdhesh Yadav
International Institute for Population Sciences (IIS), Mumbai, India
[email protected]
Laishram Ladusingh
International Institute for Population Sciences (IIS), Mumbai, India
Ezra Gayawan
Department of Mathematical Science, Redeemer’s University, Redemption City, Nigeria
Childhood malnutrition is amongst the most serious health issues facing developing countries
especially India. It is an intrinsic indicator of well-being, but it is also associated with morbidity,
mortality, impaired childhood development, and reduced labor productivity. Although there are
health inequalities in child health and survival in India, the influence of distal determinants such
as geographical location on children’s nutritional status is still unclear. We investigate the impact
of geographical location on child nutritional status by mapping the residual net effect of
malnutrition while controlling for bio-demographic and socioeconomic risk factors
simultaneously. This study utilizes the National Family Health survey data where individual data
records were constructed for children. A Bayesian geo-additive semi-parametric procedure,
which provide coherent regression framework based on Markov chain Monte Carlo Technique
was adopted and appropriate priors were assigned to the different covariates. The findings
reveal considerable geographical variation in childhood malnutrition across the states. It is
spatially structured and rates remain very high in the central region as compared to other
regions in India. Malnutrition was significantly high among male children as compared to female.
Further, results showed that birth order, consumption of Vitamin A, breastfeeding, caste, religion
and wealth quintile have significant effects on malnutrition. Childhood malnutrition is integrating
nutrition and family planning to delaying birth to ensure optimal growth, investing to ensure that
women spend more years in school, and therefore improving the socioeconomic status of
children and mother are critical in addressing malnutrition among children in India.
Key Words: Developing Countries, India, Geoaddative Regression, Markov Chain Monte Carlo
Malnutrition
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IS48: MODELLING RISK: THEORY AND APPLICATIONS
Trade Models with Risk Variables: A Review of Econometric Issues and Future Directions
Akram Shavkatovich Hasanov, PhD
Department of Econometrics and Business Statistics, School of Business, Monash University
Sunway campus. Jalan Lagoon Selatan, Bandar Sunway, 46150, Selangor Darul Ehsan,
Malaysia
[email protected]
Tel (mob): +60105111792, Tel office: (+603)-5514 6000 ext. 44455, Fax: (+603)-5514 6192
Numerous studies have attempted to investigate the hypothesis that exchange rate volatility
may have an impact on trade and investment because of its importance in the exchange rate
arrangement, trade and investment policies. However, these studies fail to offer conclusive
evidence on the true relationship. We review some recent papers and narrow down our focus
towards several specific econometric issues that are not covered in some depth in previous
survey articles. Moreover, we aim to show the validity of a set of econometric methods that are
not commonly employed in the context of an assessment of exchange rate uncertainty impact
on trade flows and investment.
Key Words: Exchange Rate Volatility, Trade and Investment
Evaluating the Soundness of Malaysian Commercial Banks
Sockalingam Ramasamy
Monash University Malaysia,
[email protected]
Nipuni Fernando
Monash University Malaysia
Balachandher Krishnan Guru
Monash University Malaysia
[email protected]
Thangarajah @ M.Thiyagarajan
Monash University Malaysia,
[email protected]
The soundness of the banking sector is integral to the economic well-being and prosperity of
any nation including Malaysia. The commercial banking sector is an important sector of the
Malaysian economy as it accounts for more than 50% of the assets of the Malaysian financial
system. This being the case, the soundness and health of the commercial banking system in
Malaysia should be continuously monitored and evaluated for its well-being. This has to done in
order to ensure that the financial intermediation process of commercial banks would continue to
operate smoothly without disruption and instability. There has been a number of financial crises
in the past decades, which had adversely affected the soundness of Malaysian banks. In this
context, the more recent crises include the Global Financial Crisis and the Asian Financial Crisis
which have amply demonstrated the vulnerability and the instability of the banking systems in
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Malaysia and the region. During such crises, governments and regulators have to spend billions
of tax payers’ money in terms of rescue and bailout packages to instil confidence among the
public and to keep the financial intermediation process in working order. Thus, it is absolutely
necessary that continuous monitoring and evaluation of banks be undertaken to ascertain the
soundness of the banks and to formulate appropriate corrective measures. This study attempts
to develop a soundness index from a set of financial and non-financial variables that could be
used as an advance indicator or early warning system to detect unsound or failing banks.
Hence, early corrective measures could be taken to avoid these weak banks from failing and
thus, keeping the cost low and avoiding a banking catastrophe. The soundness index has been
tested on the Malaysian commercial banks with promising results.
Key Words: Soundness-Index, Vulnerability, Commercial Banks, Financial Crisis, Early Warning
System.
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IS49: WELL-BEING AND QUALITY OF LIFE FOR SOCIAL PROGRESS:
MEASURES, INDICATORS AND DETERMINANTS
What Do Malaysians Care in the Pursuit of Happiness: A Cross-Sectional Ordered
Logistic/Probit Model Using World Value Survey
Ying-Yin Koay*
Department of Economics, Faculty of Business and Finance, Universiti Tunku Abdul Rahman
Jalan Universiti, Bandar Barat, 31900 Kampar, Perak
[email protected]
Tel no.: 605-4688888
Yoke-Kee Eng
Department of Economics, Faculty of Business and Finance, Universiti Tunku Abdul Rahman
Chin-Yoong Wonga
Stockholm China Economic Research Institute, Stockholm School of Economics
While material life conditions, ranging from financial security and material consumption to health
and work satisfaction are unarguably the necessary sources of wellbeing, growing empirical
results have shown that money or material goods fail to improve happiness persistently. If
money cannot buy happiness, what else can? To fill this gap, this study attempts to propose a
material-psychological compatible happiness model drawn upon Maslow’s hierarchy of needs.
In addition to economic factors, a modified model of Maslow’s hierarchy of needs permits noneconomic qualities such as belongingness, self-esteem and self-actualization, all of which have
regularly been neglected in the previous empirical studies. Based on the proposed model, this
study attempts to reveal:
(1) What do Malaysians care in the pursuit of happiness?
(2) Among the human needs, which Malaysian perceived needs bring greater impact on their
happiness?
Extracted from the World Value Survey (WVS) data with a sample of 1300 respondents from
Malaysia, we first construct a variety of components that are coherent to the theoretical model
using categorical principal component approach (CATPCA). We next test the hypothesis that
needs for physiological, safety, belongingness, self-esteem, and self-actualization help to
enhance Malaysians happiness by employing cross-sectional ordered logistic/probit approach.
Furthermore, the marginal effect analysis is used to detect which needs bring greater impact on
Malaysians perceived happiness. We last carry out a battery of sensitivity analysis to verify the
robustness of contributions of need fulfillments to happiness across genders, age and income
levels. The results shall be of interest and relevance to policymakers in formulating policies to
nurture environment for happy Malaysians.
Key Words: Happiness, World Value Survey, Maslow’s Hierarchy of Needs, Categorical
Principal Component Approach, Cross-Sectional Ordered Logistic/Probit Model
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ISI-RSC 2014 ABSTRACT BOOK
Macro Determinants of Happiness: A Panel Data Analysis
Jing Moon Chua
Monash University Malaysia, Selangor, Malaysia
[email protected]
Jason W.J. Ng
Monash University Malaysia, Selangor, Malaysia
[email protected]
The gross domestic product (GDP) has always been the traditional indicator used to measure
economic growth, with almost every country having national policies that are growth-centric in
nature. Underlying this practice is the assumption that higher income levels precede higher
levels of utility, or well-being. However, the adequacy of using economic indicators to reflect a
nation’s well-being has come under heavy scrutiny since the discovery of the Easterlin paradox,
which found that an increase in income was not associated with an increase in the level of
happiness, or subjective well-being. As such, a new economic model is required – one that
encompasses subjective well-being, one that is not solely based on consumption but also on
sustainable development and an inclusive economy. This subsequently led to an increased
awareness and discussion in looking beyond GDP and developing indicators that better
measure well-being which includes both tangible and non-tangible factors. Using the countries’
responses to the life satisfaction question in the World Values Survey as the measure of
subjective well-being over a period of time, the objectives of this study are three-fold: (i) To
identify the key macro determinants that impact happiness in a holistic approach using panel
data techniques; (ii) To investigate whether the key determinants of happiness for developed
and developing countries are different, and to identify the level of importance of each factor for
countries at different stages of development; (iii) To provide insights to policy makers into the
key factors that should be prioritized to improve the happiness level of society, especially for
developing countries where resources are constrained. The findings of this study show that the
determinants of happiness differ across developed and developing countries, with religiosity
being the only common determinant. The determinants of happiness for developed countries, in
order of importance, are: (i) Governance quality, (ii) Religiosity and (iii) Income. The
determinants of happiness for developing countries, in order of importance, are: (i) Religiosity
and (ii) Unemployment.
Key Words: GDP, Utility, Subjective Well-Being, Panel Data, World Values Survey
Building an economic indicator to measure the well-being in Egypt
Mahmoud Mohamed ElSarawy (ISI member)
Central Agency for Public Mobilization and Statistics (CAPMAS), Egypt
[email protected]
The income or GDP per capita is not the only factor to measure well-being and quality of life of
the community, there are a lot of variables that affect it, Such as availability of adequate
housing, availability of a clean healthy environment, political participation and social activities,
how to get the major needs for life, achievement of equality and availability of health care and
education services. All of these factors are measuring the community satisfaction in life
generally.
The Objectives of the study: building an indicator to measure the well-being of the community,
using many variables. The Importance of the study: stands on the extent to which the
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improvement and development of the variables used in and through make a time series of the
variables and the indicator over a number of years, and also use this indicator in international
comparisons.The study use the most recent available data for this variables, including the level
of poverty in urban and rural areas , the unemployment rate , the rate of education enrollment ,
the percentage of medical care services and the proportion of access to safe drinking water in
different regions.
Key Words: Quality of Life - International Comparisons - Community Satisfaction.
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IS50: STATISTICAL BUSINESS REGISTER ENHANCING ECONOMIC
EFFICIENCY
The Statistics New Zealand Business Register
Wan Mohd Shahrulnizam Wan Mohd Najur
Department of Statistics Malaysia, Malaysia
[email protected]
A fit for use and contemporary statistical business register is central statistical infrastructure for
a National Statistics Office. It is essential for a robust and coherent (trustworthy) Official
Statistical System. Statistics New Zealand’s Business Register (BR) in particular is central to its
core business. Almost all of Statistics New Zealand’s economic collections use the BR, either as
a frame for sample selection, as a template for integrating administrative data sources, or as a
source of standard classifications. Statistics New Zealand’s present Business Register is
outlined in this paper in the context of its external relationshIS with other government agencies
and its importance underpinning all of Statistics New Zealand’s economic and financial
statistical outputs. The background section provides summaries of i) the history of the BR; and
ii) the features of the New Zealand tax system that result in comprehensive administrative data
supplied by Inland Revenue on a regular basis. Section three focusses on the role the BR plays
within Statistics New Zealand. Effectively a two-way relationship with other Subject Matter Areas
in Statistics New Zealand, the BR is used by many teams as a sampling frame and a data
integration tool. The BR also relies on some teams providing timely information currently not
available through administration data sources. Section four focusses on the external
relationshIS important to the Business Register. While Inland Revenue and the Companies
Office are the two key external relationshIS that supply administrative data to Statistics New
Zealand, other government agencies rely on data provided by Statistics New Zealand and the
Business Register plays an important role in the preparation and provision of that information.
This paper ends with a brief look at potential opportunities to maintain the relevance of the
Business Register to the Official Statistical System in general and particularly to Statistics New
Zealand.
Key Words: Business Register, Administrative Data, Partnership
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ISI-RSC 2014 ABSTRACT BOOK
Capturing the E-Commerce Data: A Brief Study and Implementation of Business Register
in Indonesia
Erli Wijayanti Prastiwi*
Statistics Indonesia, Central Jakarta, Indonesia 10710
[email protected]
Christiayu Natalia
Statistics Indonesia, Central Jakarta, Indonesia 10710
[email protected]
Meutia Rahmah Yani Hutasuhut
Statistics Indonesia, Central Jakarta, Indonesia 10710
[email protected]
Nofrial Ardy
Statistics Indonesia, Central Jakarta, Indonesia 10710
[email protected]
Technology usage has been increasing rapidly in the past decade. From daily activities to
business, people begin to make a good use of technology to boost efficiency and save time. In
trade, people have start to move away from the use of the conventional face-to-face method of
doing business into using the internet as a place of trade and negotiation that is known as ecommerce. They use the internet to sell their product without need to provide any certain place
or formal services by which they can reduce overall cost compared if the trade done
conventionally. Notwithstanding its convenience, e-commerce brings a new problem from the
perspective of official statistics. In Indonesia, e-commerce activity has never been specifically
captured in economic census or surveys held by Statistics Indonesia although as at 2011
Indonesia has the world’s 8th largest number of internet users and this number is predicted to
increase in the following years. This highlights Indonesia as a potential e-commerce market and
increases the importance for e-commerce data to be captured in official statistics. This paper
aims to briefly explain the definition of e-commerce and ways to capture this data in Indonesia
by making a good use of Integrated Business Register of Indonesia, as the methodology
construction to capture e-commerce data has now become very notable. The contributions of
this paper are not only for the government but also for all people involved in e-commerce
market. For the government, especially Statistics Indonesia, this paper presents a new
perspective of economic activity. For all people involved in e-commerce market, statistical
analysis of their business will make the forecasting and evaluation become easier. We find that
for capturing the data, registering about 18 most popular e-commerce platforms in Indonesia will
be a very good start. Since, by capturing the platforms, the individual e-commerce data
registration will be well organized in the future.
Key Words: Official Statistics, Online, Data Register, Registration, Technology, Internet User.
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IS51: UNDERSTANDING THE MACROECONOMICS ISSUES AND
POLICY
An Empirical Study of the Transmission Mechanism and Interest Rates Pass-through:
Conventional vs Islamic
Nazirul Hazim A Khalim
Monash University Malaysia
[email protected]
This study empirically analyses the difference of impacts of shocks in the overnight interbank
money market rate on output and inflation. The focus is on establishing any significant
difference in the response of output and inflation to these shocks through two arguably distinct
nature of transmission mechanism. These differences could have important implications for the
effectiveness of monetary policy. The study found VAR and ECM evidences for the difference
in responses of GDP and CPI to shocks in the conventional and Islamic interest rates. The
short-run responses differ in direction, magnitude and timing but converge in the long-run.
Interest rate pass-through are generally larger for conventional rates but Islamic rates displays
quicker speed of adjustment. Both for conventional and Islamic, at least 50% of the passthrough are not complete. These results are consistent with reality on the ground but
inconsistent with theories claimed by Islamic finance advocates. Using monthly Conventional
and Islamic Interbank Money Market rates from 2000M01 to 2013M06, unrestricted 9-variable 8lag open economy VARs were used to trace the effects of shocks in interest rates on output and
inflation. Cointegration was used to examine the long-run differences in the transmission
mechanism of conventional and Islamic rates. The Engle-Granger Error Correction Model
(ECM) was used to estimate the immediate, long-run and speed of adjustment of the interest
pass-through. Rolling regression was used to evaluate the consistency of interest rates passthrough coefficient estimates over time. This study offers some insight into the macrodynamics
of an Islamic economy. It does not, however, provide insight into the microbehaviour of
economic agents in an Islamic economy. Microfounded models such as the Dynamic Stochastic
General Equilibrium (DSGE) could offer some answers and is an appropriate extension to this
research.
Key Words: Transmission Mechanism, Interest Rates Pass-Through, Conventional, Islamic,
Vector Auto Regression, Error Correction Model
Examining The Exchange Rate Regime-Monetary Policy Autonomy Nexus: Evidence
From Malaysia
Soo Khoon Goh, Robert McNownb
Centre for Policy Research & International Studies, Universiti Sains Malaysia, 11800, Penang,
Malaysia
University of Colorado, Boulder, Co, 256 UCB 80309, United States.
[email protected]
Some studies suggest that Asian countries can be exempted from the impossible trinity. We
examined the empirical relevance of this argument by reviewing the experience of Malaysia. We
defined monetary autonomy by analyzing the interaction between the Malaysian and US interest
rates.We used the Unrestricted Error Correction Model Pesaran Bound test to analyze the
interaction between the Malaysian and US interest rates during three different sub periods. Our
empirical results showed that there is no cointegration evidence during the managed floating
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exchange rate periods. However, we found that there was a level relationship between the
Malaysian and US interest rates during the period when Malaysia had a fixed exchange rate
and an open capital account regime. In contrast with other existing studies, we conclude that
Malaysia is not exempted from the impossible trinity. Our study has also highlighted that the
Pesaran Bounds test must be interpreted carefully when applied to series with mixed orders of
integration.
Key Words: Interest Rate Interaction, Monetary Autonomy, Malaysia
The Transmission of Financial Stress and Monetary Policy Responses In The ASEAN-5
Economies
Tng Boon Hwa
Bank Negara Malaysia
[email protected]
Since the 2008 Global Financial Crisis, there has been a resurgence of interest in the linkages
between the economy and financial markets. This paper analyses the determinants of financial
stress, the impact of financial stress on the real economy and the relationship between
monetary policy and financial stress in the ASEAN-5 economies. I estimate a panel model of the
determinants of financial stress, and find that US financial stress, financial contagion within the
region and domestic credit emerge as important sources of stress. Through a subsequent VAR
analysis, I find that financial stress has adverse effects on the real economy, with the largest
effects occurring in the first six months after the financial shock. In response to higher financial
stress, the central banks in Malaysia, the Philippines and Thailand tend to reduce their policy
rates beyond other macro-financial considerations. Although lower policy interest rates have
limited effects in alleviating financial stress (except in Malaysia), they are effective in stimulating
economic activity through other channels.
Key
Words:
Financial
stress;
Financial
spillovers;
Monetary
policy
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IS52: ROBUST STATISTICS
Comparing PRM, PLS, PCR and RR Techniques To Handle Multicollinearity and Outliers
Norazan Mohamed Ramli,
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah
Alam, Selangor, Malaysia
[email protected]
Nor Hidayah Mohd Noh,
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah
Alam, Selangor, Malaysia
Mazni Mohamad,
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah
Alam, Selangor, Malaysia
Nor Azura Md Ghani
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah
Alam, Selangor, Malaysia
In regression, ordinary least squares (OLS) estimator may fail if the variables are almost or
completely collinear. This phenomenon is called multicollinearity which increases chance of
independent variable to be rejected from regression model as an insignificant variable and may
cause the estimated OLS coefficients to be statistically insignificant even if the value of Rsquare may be high. The OLS regression may also yield unstable results due to increasing
standard error of their estimated coefficients. In addition, the presence of outliers worsens the
problem and this can change the magnitude of regression coefficients and even the coefficient
signs. Handling multicollinearity problem in regression analysis is important because least
squares estimation assumes that predictor variables are not correlated with each other.
Therefore, reliable techniques are required to reduce or remove the effect of outlying data points
in the presence of multicollinearity problem. Thus, in this paper a comparison of several
regression techniques was carried out to determine the best regression method in handling
these problems. The performances of ridge regression (RR), principal component regression
(PCR) and partial least squares regression (PLS) in handling multicollinearity problem in
simulated data and real sets were compared to the classical ordinary least squares (OLS)
regression in order to help and give future researchers a comprehensive view about the best
procedure to handle multicollinearity problem. Partial Robust M-Regression (PRM) that can deal
with multicollinearity and outliers simultaneously was also employed in this study. For
comparison purposes, standard error of prediction (SEP), root mean square error (RMSE) and
mean square error of β estimate (MSE(β)) were calculated. Results show that the PRM
regression is the best method in the presence of multicollinearity and vertical outliers.
Key Words: Multicollinearity, Outliers, Partial Robust M-Regression
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IS53: MODELLING AND ANALYSIS WITH APPLICATIONS – II
Goodness of Fit Test for Semiparametric Logistic Regression for Correlated Binary Data
Suliadi*
Dept. of Statistics Bandung Islamic University, Bandung, Indonesia
[email protected]
Abdul Kudus
Dept. of Statistics Bandung Islamic University, Bandung, Indonesia
In this paper we consider goodness of fit test for semiparametric models, when the data are
binary and correlated. We work under the frame of generalized estimating equation (GEE) and
the nonparametric component is estimated using smoothing spline. We consider the extension
of goodness of fits test from parametric GEE model to semiparametric GEE model. The focus of
our study is the methods based on residual. For residual based method we obtained that the
extension of the method has good performance to detect correct models, but very low power to
detect incorrect models.
Key Words: Residual-Based Goodness of Fit Test, Covariate-Based Partitioning Goodness of
Fit Test, Detection of Correct & Incorrect Model
Stocks Network Analysis of KLSE: A Multivariate Time Series Similarity Approach
Maman A. Djauhari
Institute for Mathematical Research (INSPEM), Universiti Putra Malaysia, 43400 UPM Serdang,
Selangor Darul Ehsan, Malaysia
[email protected]
Gan Siew Lee
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia,
81310 UTM Skudai, Johor, Malaysia
[email protected]
Escoufier’s operator will be used to develop the notion of similarity among multivariate time
series of stocks’ prices to analyze the behavior of stocks market in multivariate setting. Then,
stocks network will be constructed to represent stocks market behavior. To filter the economic
information contained in the network, the standard tools in econophysics, i.e., minimal spanning
tree will be used. As an example, KLSE data will be investigated and discussed to illustrate the
advantages of multivariate approach. We show that this approach can better figure out the real
situation in KLSE compared to the standard approach.
Key Words: Centrality Measures, Minimal Spanning Tree, Multivariate Time Series, RVCoefficient, Social Network Analysis
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ISI-RSC 2014 ABSTRACT BOOK
A Bivariate GLM with Application
M. Ataharul Islam
University of Dhaka, Bangladesh
[email protected]
Rafiqul I Chowdhury
Department of Applied Statistics, East West University
Dependence in outcome variables may pose formidable difficulty in analyzing data in
longitudinal studies. The univariate and bivariate geometric probability distributions have an
increasingly important role in various fields, including the reliability and survival analysis. A
bivariate geometric model with covariate dependence is proposed in this paper with an
example. The estimation procedure is illustrated using the extended generalized linear model
approach.
Key Words: Bivariate Geometric Model, Reliability and Survival Analysis
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IS54: RESEARCH USING STRUCTURAL EQUATION MODELLING
Discriminant Validity Assessment: On Fornell & Larcker Criterion versus HTMT Criterion
Mohd Rashid bin Ab Hamid
Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun
Razak, 26300 Gambang, Kuantan, Pahang, Malaysia
[email protected]
Assessment of discriminant validity is a must in any research that involved latent variables in
order to prevent the multicollinearity issues and misleading interpretations of structural path.
Usually, Fornell and Larcker criterion is the most widely used method for this purpose. However,
new method has emerged for establishing the discriminant validity assessment through
heterotrait-monotrait (HTMT) method. Therefore, this article presents the results of discriminant
validity assessment using these two methods. Data from previous study were used that involved
429 respondents for empirical validation of instrument for value-based excellence model in
higher education institutions (HEI) in Malaysia. From the analysis, the convergent, divergent and
discriminant validity were established and admissible using Fornell and Larcker criterion.
However, the discriminant validity is an issue when employing the HTMT criterion. This shows
that the latents variables under study faced the issue of multicollinearity and should be looked
into further. Thus the study of structural relationshIS cannot be interpreted correctly if the
researcher proceeded to the inner model assessment. This also implied that the HTMT criterion
is a stringent measure that could detect the possible indiscriminancy among the latent variables.
In conclusion, the instrument which consisted of six latent variables was still lacking in terms of
discriminant validity and should be explored further.
Key Words: Discriminant Validity Assessment, Criterion
Invariance Analysis for Establishing External Validity of Islamic Maternal Attachment
Model
Siti Aishah Hassan
Department of Counsellor Education and Counselling Psychology, Faculty of Educational Study,
Universiti Putra Malaysia
[email protected] or [email protected]
This study aims to examine the external validity of the Islamic Maternal Attachment Model that
is hypothesized as God-mother attachment significantly influences mother-child attachment.
The study was conducted in two phases on Malaysian students aged from 13 to 17 years old.
For phase I, n =195 from private religious schools only; for phase II, n=1182 from public daily
schools. Inventory of Parental Peer Attachment (IPPA) was used to measure mother-child
attachment and Maternal Spiritual Characteristics Scales (MSCS) to measure God-mother
attachment. Findings indicated both models are admissible and invariance for both populations,
GFI, AGFI, CFI, IFI >0.900, RMSEA=0.035; ΔCFI =0.004. Thus external validity of the Godmother-child attachment model was established among Malaysian Muslim family.
Key Words: Islamic, Maternal Attachment, Mother, Child
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ISI-RSC 2014 ABSTRACT BOOK
IS55: TRACKING SOCIO-DEMOGRAPHIC CHANGES IN MALAYSIA
Employment of Women in Malaysia- Four Decades of Change
Low Shu Yik
University of Malaya, Kuala Lumpur, Malaysia
[email protected]
Malaysian women have made remarkable progress in terms of education, health and economic
participation. Greater involvement of women in the labour market has contributed immensely to
national development. The roles and contributions of women have been duly recognized. This
led to the adoption of a policy objective of increasing the number of women in key decisionmaking positions in the 10th Malaysia Plan (2011-2015). This paper, based on the 2 percent
census data, aims to analyze the trend and pattern of women’s employment between 1970 and
2000. What has been the trend of female labour force participation rate in Malaysia over the
past few decades? What are the sectors that employed female workers? What are the factors
that facilitate or inhibit women’s participation in the workforce? Our results indicate that female
labour force participation remained low although they have overtaken the men in secondary and
tertiary education. Many women drop out of the labour market due to family reasons during their
child-bearing years. Women are also over-represented in the low-skilled and low-wage jobs.
Key Words: Female Employment, Logistic Regression, Socio-Demographic Changes
Family Support for Older Malaysians
Jane Teh Kimm Lii*
University of Malaya, Kuala Lumpur, Malaysia
[email protected]
Ng Sor Tho
University of Malaya, Kuala Lumpur, Malaysia
[email protected]
Family support for older persons and their well-being are important concerns for Malaysia. Adult
children are especially important in providing financial support, care and emotional needs for
their parents. Adult children’s more frequent contact, care and affection have also been found to
lessen the experience of loneliness in their parents. This study examines the patterns of family
support (support from adult children) for older Malaysians across socio-demographic subgroups
using data from the 2004 Malaysian Population and Family Survey. It also examines factors
which lead to more family support, and if family support has an ameliorating effect on loneliness
among older Malaysians. Increased needs in older parents (low SES or health/physical
limitations) and the quality of the relationship with children were examined as possible factors
which influence family support. Descriptive statistics and the SmartPLS 2.0 variance-based
Partial Least Squares statistical software were used in analyses. Our results indicate that SES
of older Malaysians and their relationship with children affect the provision of support. However,
the quality of the relationship with children is the main factor and predictor of receiving support
from adult children. This support, in turn, reduced loneliness among older Malaysians.
Key Words: Family, Adult Children, Support, Loneliness
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ISI-RSC 2014 ABSTRACT BOOK
IS56: APPLIED STATISTICS IN INSURANCE
Risk Diversification in Insurers Under Stress Scenarios
Jim Qin
Singapore Actuarial Society
[email protected]
Diversification between lines of business, countries, asset & liability is often difficult to quantify
for actuaries. This field of research has been conducted in more developed insurance markets
such as Australia, the UK and America. One of the more notable works is the “APRA Risk
Margin Analysis” by Scott Collings and Graham White, where the correlation matrix been cited
and used as the industry benchmark for reserve calculation.
The motivation behind our research is to address the question of an increased correlation during
stressed scenarios, and whether we can still assume some level of diversification at a higher
correlation level. Our work can be split into three main parts:
1. Quantification of correlation of various risks. We looked at correlation between lines of
business, between investment asset classes and also between insurance risk and
investment risk. This is done, as much as possible, within the context of the Singapore.
2. Application of a suitable dependency structure to mimic higher level of dependency under
stressed environment.
3. Quantifying the level of diversification benefit given an increased level of dependency and
correlation, using a pseudo company set-up.
Preliminary results showed that correlation between lines of business are mainly weakly
correlated with a few lines having moderate level of correlation. Investment assets have
generally moderate to high correlation and also display much higher correlation during
economic stress period. Investment and insurance risk are also weakly correlated. This leads to
a considerable amount of diversification benefit calculated from our pseudo company.
As our analysis is done on empirical data, the lack of insurance-risk induced economic downturn
or the lack of insurance catastrophe could distort the correlations we observed. We included
several sensitivity tests to validate the robustness of our observation. The sensitivity tests
include:
1. Setting varying minimum correlation between line of business and also between line of
business and assets.
2. Changing the underlying loss distribution assumed.
3. Varying the number of lines in the pseudo company.
4. Varying the business mix in the pseudo company.
While the sensitivity tests had big impact on the amount of diversification benefit calculated for
the pseudo company, the overall conclusion was the presence of diversification benefit even
during stressed scenario. Risk diversification in insurers under stress scenarios
Key Words: Risk Diversification, Insurance, Actuaries
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Current Application of Statistical Techniques to the Malaysian Actuarial Profession
Kelvin Hii Chee Yun, FIAA
Lonpac Insurance Bhd & Actuarial Society of Malaysia
Kuala Lumpur, Malaysia
[email protected]
Seow Fan Chong, FSA, FIA
Department of Financial Mathematics and Statistics
Business School - Sunway University
Selangor Darul Ehsan, Malaysia
[email protected]
Actuaries in Malaysia have been applying statistical techniques to their fields of practice for
more than 30 years. In recent years, partly due to rapid worldwide and local regulatory changes,
there are increasingly more areas of actuarial use for statistical or stochastic approaches rather
than deterministic. We introduce the current common statistical techniques used by Malaysian
actuaries in the general and life insurance business. These techniques include bootstrapping,
parametric models, Generalized Linear Models (GLM) and simulation. Finally, we introduce
some statistical techniques, applied by actuaries outside Malaysia, which are likely to be applied
to the Malaysian insurance industries in the near future.
Key Words: Life Insurance, General Insurance, Malaysian Actuaries
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CONTRIBUTED PAPER SESSIONS
CPS01: STATISTICAL THEORY AND METHODOLOGY I
A Note on Shrinkage Kernel Density Estimation
M. Arashi*
Department of Statistics, School of Mathematical Sciences, Shahrood University, Shahrood,
Iran
[email protected]
M. Mahmoodi
Department of Statistics, School of Mathematical Sciences, Shahrood University, Shahrood,
Iran
[email protected]
A shrinkage kernel density estimator is defined, where an adjusted kernel density is shrunken
according to a suspicious null density. The asymptotic performance of the proposed estimator is
studied under local alternatives. Optimum values of parameters for which the proposed
shrinkage estimator performs better than the adjusted estimator are discussed. It is shown that
the proposed estimator asymptotically performs better, in the sense of having smaller
asymptotic relative efficiency, for specific parameter selections.
Key Words: Asymptotic Distributional Mean Square Error, Band-Width, Kernel Density
Estimator, Shrinkage Estimator, Shrinkage Factor.
A Comparative Study on Different Class of Optimal Design Criteria
Md. Shaddam Hossain Bagmar*
University of Dhaka, Dhaka, Bangladesh
[email protected]
Dr. A H M Mahbub Latif
University of Dhaka, Dhaka, Bangladesh
[email protected]
In this paper, different optimal designs are compared considering pure error degrees of freedom
and robustness properties under model assumptions. Standard and modified criteria result
extreme designs that does not allow pure error estimation and no degrees of freedom for
checking lack of fit respectively. On the other hand, compound criteria construct compromise
designs allowing pure error estimation as well as lack of fit checking. Among the competing
designs, compound criteria based optimal designs are found to be the most robust under model
assumption and could be more useful in practice because it can take into account the multiple
objectives.
Key Words: Standard Criteria, Modified Criteria, Compound Criteria, Lack-of-Fit, Pure Error,
Robustness Property.
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A Procedure to Attaining Non-Inferiority Margin
Gajendra K. Vishwakarma
Department of Applied Mathematics, Indian School of Mines, Dhanbad - 826004, Jharkhand,
India
[email protected]
Non-inferiority trials are clinical trials in which the goal is to demonstrate that the effect of an
experimental treatment is not inferior to that of an active control by more than a specific margin.
Often, a secondary goal is to indirectly compare the experimental treatment with placebo. The
most common situation where this occurs is when the active control has been previously studied
versus placebo, but including a placebo in a new trial would be considered unethical. In some
cases this indirect evidence for an effect versus placebo has been used as evidence for
regulatory approval of a new therapy. This paper proposes a procedure for statistical
consideration to fixing the non-inferiority margin when placebo effect is not available. In
addition, we derive the optimal allocation that minimizes the total sample size.
Key Words: Non-Inferiority Clinical Trial, Level of Significance, Confidence Interval, NonInferiority Margin, Sample Size.
A Study on the Simultaneous Tests under the Normality
Hyo-Il Park
Department of Statistics, Chongju University, Chongju 360-764, South Korea
[email protected]
In this study, we propose a likelihood ratio simultaneous test for the mean and variance for the
two-sample problem when the underlying distribution is normal. Then we consider the union
intersection test with the jointly likelihood ratio statistics. Also, we consider using the combining
functions to combine individual tests for each sub-null hypothesis. For obtaining the null
distributions, we apply the permutation principle. Then we compare the efficiency among the
proposed tests through a simulation study. Finally, we discuss some interesting features related
to the simultaneous test as concluding remarks.
Key Words: Combining Function, Likelihood Ratio Principle, Monte-Carlo Method, Permutation
Principle, Union-Intersection Test
Characterising Gumbel Autoregressive Process
Bako Sunday Samuel
Universiti Putra Malaysia
[email protected]
Gumbel distribution is the possible limit for the entire range of tail behaviour between polynomial
decrease and essentially a finite endpoint and it is known to fit well in many situations. In this
paper, we assess the performance of the maximum likelihood estimate of the parameters of a
Gumbel Autoregressive process and evaluate the time series forecast accuracy. The
Generalised Pareto distribution is fitted to the Gumbel time-dependent series and the
performance of the estimates of the parameters fitted to the cluster maxima and the original
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series is assess. A bootstrap sample sizes with varying levels of dependence is generated from
stationary Gumbel autoregressive process. Time dependence is induced through a linear filter
process with Gumbel distribute innovations. Ignoring the effect of dependence leads to
overestimation of the location and underestimation of the scale parameters respectively. As
dependence is enhance, forecast accuracy improves. Forecast accuracy decrease when the
sample size increases for both a fixed and decreasing dependence level. The shape and scale
parameter of the Generalised Pareto distribution (GPD) fitted to the cluster maxima and to the
original series perform better for an increasing sample size, however, the shape and scale
parameters of the GPD fitted to the time-varying Gumbel distribution is underestimated when
the effect of dependence is ignore.
Key Words: Gumbel, Dependence, Parameters, Distribution
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CPS02: ECONOMICS AND ECONOMETRICS I
The Marginal Propensity to Consume Across Household Income Groups
Ang Jian Wei,
Bank Negara Malaysia, Kuala Lumpur, Malaysia
[email protected]
Dhruva Murugasu
Bank Negara Malaysia, Kuala Lumpur, Malaysia
[email protected]
T’ng Boon Hwa
Bank Negara Malaysia, Kuala Lumpur, Malaysia
[email protected]
Understanding heterogeneity in the way households respond to income changes is crucial for
policymaking, as shocks in the economy often affect specific groups of households differently.
Using household-level data from the Household Expenditure Survey (HES), this paper
estimates the marginal propensity to consume (MPC) out of disposable income for Malaysian
households and examines how the propensities differ across income brackets. We find
evidence that the MPC out of disposable income for lower income households is higher than
that for higher income households. The MPCs vary from 0.81 for those earning below RM1,
000 to 0.25 for those earning above RM10, 000. These MPCs allow policymakers in Malaysia to
estimate more precisely the aggregate consumption effects of income shocks that affect
households of specific income groups.
Key Words: Income, Households, Marginal Propensity to Consume
An Econometrics Model for the Determinants of Manufacturing Production's Volatility in
Malaysia
Abouellial, Embareka*
Statistical Researcher & National Accountant
National Accounts Dept., Central Agency for Public Mobilization and Statistics (CAPMAS), Cairo
(2086), Egypt
[email protected]
Manufacturing is considered a strategic sector that could help achieve objectives of transition
economies as activities in this sector could help eradicate poverty by creating employment
opportunities. The dynamic economic growth and development experienced in most developing
Asian economies have been achieved through industrialization. A good example is the case of
Malaysia. Since the implementation of the first Malaysian development plan in the 1960s, the
manufacturing sector has been the most rapidly growing sector. However, globalization raises
uncertainties, such as high oil prices and the weakness of the U.S. dollar. Thus, achieving a
sustainable growth in manufacturing production has become very difficult. Empirically, previous
studies have generally found that the manufacturing sector is a key identifier of economic
growths, however, they rarely controlled for a number of explanatory variables beyond the
determinants of the manufacturing growth sector. In this regard, this paper aims to build an
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econometric model for the determinants of Malaysian manufacturing production's volatility by
first investigating the macroeconomic policies affecting the manufacturing production growth.
Secondly, the study attempts to identify key policies contributed to shaping the risks on volatility
of the manufacturing production. The data used for the model are the annual time-series of
Malaysia from 1970 to 2012. The data on manufacturing production are collected from the
Annual Statistics of Manufacturing Industries, Malaysian national statistical office. The study
uses manufacturing industries index as it is the most commonly used measurement for
manufacturing production. The manufactured volatility is computed based on the International
Monetary Fund (IMF) framework as standard deviation of monthly changes in manufactured
production over the entire five years involved in each observation to capture the long run
volatility. The model is designed based on the Vector Error Correction (VEC) model in order to
explore the short run and the long-run equilibrium relationship as well as the causality between
the dependent variable and the explanatory variables. Among the important findings, the
Malaysian government plays a key role in driving Malaysia’s manufacturing sector. Moreover,
the price stability has a high impact on the Manufacturing volatility.
Key Words: Government Expenditure, Manufacturing Industries Index, Gross Capital Formation,
Producer Price Index, and Financial Development.
Electricity Consumption and Economic Growth in Malaysia: A Multivariate Threshold CoIntegration Analysis Approach
Ahmad Rafdi Endut
Senior Analyst
Institute of Strategic and International Studies
[email protected]
This paper tries to establish the long-run sustainability between real GDP and disaggregate
electricity consumption when the relationship is considered as a non-linear framework. Our
empirical methodology makes use of the recent developments on the multivariate threshold
cointegration test developed by Hansen and Seo (2002) that consider the possibility of a nonlinear dynamic process between output and disaggregated electricity consumption. The
analysis is applied to the case of Malaysia. Results indicate that Malaysia would be in favor of a
non-linear cointegration for electricity consumption in both residential and industrial sectors.
With these types of nonlinear frameworks, policy makers are able to identify the expectations
mechanism of electricity dependencies of economic growth.
Key Words: Electricity consumption, Non-linear, Cointegration
Wage Inequality and Trade Reforms in Malaysia: An Empirical Study
Juita Mohamad (Ph.D)
Institute of Strategic and International Studies (ISIS), Malaysia
[email protected]
The paper investigates the effects of drastic tariff rate reduction on wage inequality between
skilled and unskilled workers from 1989-2009 in Malaysia. Three channels were selected in
influencing wage distribution in Malaysia: the education premium, occupation premium and
industry premium channels. Using Malaysian Household Income Survey data and trade data,
the author employed a two-stage estimation framework which is widely used in the investigation
of wage inequality issues. Inspired by Attanasio, Goldberg and Pavcnik (2004), in the first stage,
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the log of worker`s wages is regressed on a vector of worker`s characteristics and industry
indicators. The wage premium is the coefficient of the industry dummy that explains the
differences in wages. The wage premium is the coefficient which can only be explained by the
worker`s industry affiliation. Using the Haisken-DeNew and Schmidt (1997) two-step restricted
least squared procedure, the normalized wage differentials and the standard errors are
calculated. The author found that trade reforms have led to decreased wage inequality in
Malaysia in the period observed. The impact of trade reform is significant but quite small.
Key Words: Tariff Reduction, Skilled Workers, Unskilled Workers, Wage Premium, Bumiputeras
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CPS03: STATISTICAL THEORY AND METHODOLOGY II
A Modified Rotnitzky- Jewell Criteria for Selecting Correlation Structure for Generalized
Estimating Equations
Ajmery Jaman*
James P Grant School of Public Health, Dhaka-1212, Bangladesh
[email protected]
A.H.M. Mahbub Latif
University of Dhaka, Dhaka-1000, Bangladesh
[email protected]
Wasimul Bari
University of Dhaka, Dhaka-1000, Bangladesh
[email protected]
Abdus S. Wahed
University of Pittsburgh, Pittsburgh, PA 15261, U.S.A
[email protected]
The method of generalized estimating equations (GEE) models the correlation between the
repeated observations on a subject with a working correlation matrix. Correct specification of
the underlying structure is a potentially beneficial goal, in terms of improving efficiency and
enhancing scientific understanding. For analyzing longitudinal data, a number of criteria are
available in the literature for selecting an appropriate working correlation structure in GEE. The
Rotnitzky-Jewell (RJ) criteria considered in this study are based on their good performance,
which in turn are based on the fact that if the assumed working correlation structure is correct
then the model-based (naive) and the sandwich (robust) covariance estimators of the regression
parameter estimates of the marginal model should be close to each other. In this article, we
propose a modification to the RJ criteria based on the bias-corrected sandwich covariance
estimator and show a comparison between the proposed criteria and the competing approaches
using simulation studies with correlated binary responses. The results revealed that the
proposed bias correction approach brings improvement in the RJ criteria in terms of improving
the percentage of selection of the correct correlation structure. We also provide an illustration of
our findings using a longitudinal data set from Indonesian Children's Health Study.
Key Words: Bias-Corrected Sandwich Covariance Estimator, Correlated Binary Response,
Longitudinal Data, Model-Based Covariance Estimator
Generalized Concept of Relative Risk and Wider Applications of the Proportional Hazards
Model and the Kaplan-Meier Estimator
Bojuan Barbara Zhao
Department of Statistics, Tianjin University of Finance and Economics, Tianjin, P.R. China
Email: [email protected]
The concepts of relative risk and hazard ratio are generalized for ordinary ordinal and
continuous response variables respectively. Under the generalized concepts, the Cox
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proportional hazards model with the Breslow's and Efron's methods can be regarded as
generalizations of the Mantel-Haenszel estimator for dealing with broader types of covariates
and responses. When ordinal responses can be regarded as discretized observations of a
hypothetical continuous variable, the estimated relative risks from the Cox model reflect the
associations between the responses and covariates. Examples are given to illustrate the
generalized concepts and wider applications of the Cox model and the Kaplan-Meier estimator.
Key Words: Cox Proportional Hazards Model, Kaplan-Meier Estimator, Mantel-Haenszel
Estimator, Relative Risk, Hazard Ratio
New Bivariate Zero-Inflated Negative Binomial Regression Models with Flexible
Correlation Structure
Pouya Faroughi*
University of Kebangsaan, Kuala Lumpur, Malaysia
[email protected]
Noriszura Ismail
University of Kebangsaan, Kuala Lumpur, Malaysia
[email protected]
Count data often display excessive number of zero outcomes than are expected in Poisson
regression. Zero-inflated Poisson (ZIP) regression has been suggested to handle purely zeroinflated data, whereas zero-inflated negative binomial (ZINB) regression has been fitted for
zero-inflated data with additional overdispersion. For bivariate and zero-inflated data, several
regression models such as bivariate zero-inflated Poisson (BZIP) and bivariate zero-inflated
negative binomial (BZINB) have been considered. This paper introduces two forms of BZINB
regression which can be fitted to bivariate and zero-inflated count data. The main advantages of
having several forms of BZINB regression are allow to choose the best model, they have flexible
forms of mean-variance relationship, they can be fitted to bivariate zero-inflated count data with
positive, zero or negative correlations, and they allow additional overdispersion of the two
dependent variables.. In the application section is provided numerical illustration where BZIP
regression and new form of BZINB regression are fitted to Australian health survey data.
Key Words: Bivariate Counts, Zero-Inflation, Negative Binomial, Correlation.
Extended Cox Regression Model in Case of Violation of the Proportionality Assumption:
Heaviside Function Approach
Md. Arif Rahman*
University of Dhaka, Dhaka-1000, Bangladesh
[email protected]
Md. Rashedul Hoque
University of Dhaka, Dhaka-1000, Bangladesh
[email protected]
The area of survival analysis has been studied extensively during the latter half of the 20th
century. The Cox regression model (semi-parametric methods) has been widely used in
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survival analysis for examining the covariate effects on the hazard function via the log-rank test
for comparing the equality of two or more survival distributions. The first objective of this paper
is to check proportionality assumption of such Cox model. Secondly, we use the Extended Cox
model with time-dependent covariates and fit more flexible model than usual Cox model by
using Heaviside function which is used to deal with non-proportionality of hazards when this
assumption is violated. We use discrete time function (𝑡) as Heaviside function in different ways
to fit the Extended Cox model. We apply these methods to time to first birth given by the
women after marriage data, taken from BDHS 2011 women data, which is freely available in
MEASURE DHS website. The Extended Cox model performs better as expected compared to
Cox model while fitting the data.
Key Words: Proportionality Assumption, Time Dependent Covariate, Heaviside Function.
Utilization of a Known Coefficient of Variation in the Normal Variance Interval Estimation
Procedure
Sirima Suwan
Chiang Mai University, Chiang Mai, Thailand
[email protected]
Interval estimations of population variance using the known coefficient of variation have been
derived under the usual assumptions of normality. Three methods for constructing the variance
confidence intervals are compared through an empirical simulation study.
Key Words: Coefficient of Variation, Variance, Confidence Interval, Normality.
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CPS04: ECONOMICS AND ECONOMETRICS II
Assessing the Operational Performance of Orissa Power Generation Corporation Limited
Through Slack Based Model
Amritpal Singh Dhillon
Research Scholar
Hemchandracharya North Gujarat University, Gujarat, India
[email protected]
Dr. Hardik Vachhrajani
Associate Professor
Faculty of Management, Amrita Vishwa Vidyapeetham (University), Amritapuri Campus, Kerala,
India
[email protected]
Jasleen Kaur
Assistant Professor
Shroff S. R. Rotary Institute of Chemical Technology (SRICT), Gujarat (India)
[email protected]
+91 8128672321
The operational well-being of the power generation sector is crucial for the financial viability of
the entire value chain of the power sector. The objective of this research is to assess the
operational performance of the Orissa Power Generation Corporation Limited which is involved
in the generation and transmission of power to end consumers. Slack based model proposed by
Tone (2001) was used to assess operational performance. The results showed that 25% DMUs
are efficient and have a constant return to scale while the remaining 52% inefficient DMUs have
an increasing returns to scale. Slack based model also helped inefficient DMUs in identifying the
inputs slack and ways to become efficient. Results of this research paper is going to be useful
for policy and decision makers in order to achieve better overall performance and providing
reliable energy at reasonable rate to final consumers or society at large.
Key Words: Data Envelopment Analysis, Input Inefficiency, Scale Efficiency, Benchmarking.
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Developing an Algorithm for Making Profitable IPO Investment Decisions: Evidence from
Malaysia
Thangarajah. M.
School of Business, Monash University Malaysia
[email protected]
Balachandher Krishnan Guru
School of Business, Monash University Malaysia
Sockalingam Ramasamy
School of Business, Monash University Malaysia
Researchers in initial Public Offerings have predominantly been concerned with the
phenomenon of under-pricing in the early years. The interest in initial Public Offerings (IPO’s)
was primarily driven by the extremely large returns on the first day of listing. The phenomenal
first day returns were so evident that initial public offerings (IPO’s) was anecdotally referred to
as Instant Profit Opportunities. More recent evidence from Malaysia and across the globe
seems to indicate declining first day returns thus raising doubts about the profitability of
investment in IPO’s. In the context of the foregoing discussion, this study is an attempt to
develop an algorithm centred on valuation of IPO’s for making profitable IPO investment
decisions. To this extent, two separate valuation methods are used to estimate a fair value for
the IPO shares and based on these estimates a decision algorithm is developed for making
profitable IPO related investment decisions. In addition, the sample data is further sub-divided
into two separate sets. One set of data is used to develop the model for estimating the value of
the IPO firms and the other set is used as a holdout sample to test the validity and profitability of
the IPO investment decision algorithm.
Key Words: IPO, Investment, Algorithm
Divergence of International and Domestic Prices of Imported Edible Oil in a Small Open
Economy: The Case of Bangladesh
Md. Amzad Hossain
Lecturer
Department of Economics, University of Dhaka
[email protected]
M. A. Taslim
Professor
Department of Economics, University of Dhaka
Market supplies of many essential commodities in Bangladesh, such as edible oil, consist of
mostly imports since domestic production is meagre. An alleged peculiarity of the pattern of
price variations of these commodities is that when the international prices go up, the domestic
prices respond positively almost immediately, but the domestic prices do not show the same
fluidity when the world prices go down. It is frequently alleged that collusion among the
business people prevents price flexibility in the downward direction. Using time series
cointegration, this paper finds that domestic price is cointegrated with international price in line
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with small country assumption. However, test of asymmetry suggests that positive price shock
is transmitted at a faster rate than the negative ones fuelling the “cartel” argument. However,
We investigate the soybean oil market in depth and find that the behaviour of the soybean oil
price can be explained by the interplay of competitive market forces in the specific context of the
edible industry of Bangladesh. The level of stocks, price and supply expectations and the
particular structure of the domestic edible oil market of Bangladesh are all contributed to the
evolution of the soybean oil prices during the period under investigation.
Key Words: Price, Domestic, Commodities
Traffic Highway Development: An Analysis Based on Time Series Similarity Approach
Norhaidah Mohd Asrah
Faculty of Science, Technology & Human Development, Universiti Tun Hussein Onn Malaysia,
Malaysia
[email protected]
Maman Abdurachman Djauhari
Center for Research, Consultation and Training in Statistical Analysis, Universitas Pasunda,
Indonesia
[email protected]
One of the most important elements for the overall economic development in any country all
over the world is road development. When roads have a better interconnected network, it will
help the economic activities such as trade to increase. As the country developed, the demand
for efficient road transportation will get higher and higher and the highways are required to
overcome this situation. In Malaysia, the development of highway is part of government
commitment to support the rapid growth of the economy so that it will increase the quality of life
in the society. PLUS Malaysia Berhad is one of the highway operators in Malaysia. It is also
the largest highway concessionary or build–operate–transfer (BOT) operator company in
Malaysia. In this paper, the network analysis of PLUS traffic highway development are studied.
The data used is based on the number of vehicles enters and exits for each toll plaza from toll
plaza Juru, Pulau Pinang to toll plaza Skudai, Johor Baru. The analysis of these data is based
on the measure of similarity among time series of highway traffic that will be defined. The
interrelationshIS amongst toll plazas are studied by using minimum spanning tree (MST). The
interpretation of MST is conducted by using centrality measures usually used in Social Network
Analysis. Based on the results of this study, some recommendations related to traffic highway
development are delivered.
Key Words: Betweeness Centrality, Correlation Network, Degree Centrality, Network Topology,
Pearson Correlation Coefficient
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CPS05: STATISTICAL THEORY AND METHODOLOGY – III
Objective Priors for the Zero-Inflated Model
Ryunosuke Tanabe*
University of Osaka, Osaka, Japan
[email protected]
Etsuo Kumagai
University of Osaka,Osaka, Japan
[email protected]
Zero-inflated model is applied on data with excess zero values which is often seen in medical
data, and this model is formulated in two ways: one is "with zeros model" which is a mixture of a
non-negative discrete distribution and a zero point distribution, and the other is "hurdle model"
which is a mixture of a zero truncated non negative discrete distribution and a zero point
distribution. In the Bayesian statistics, objective Bayesians have a tendency to use objective
priors. Famous objective priors are the Jeffreys prior, a reference prior and a matching prior.
There is a problem that objective priors are not derived in many zero-inflated models, so that
data analysis in these models cannot be conducted from the objective Bayesian viewpoint. We
show that the Jeffreys, reference, and matching priors as objective prior distributions are
theoretically derived in the hurdle model and with zeros model. Furthermore, we derive the
posterior distributions and posterior means for their models and obtain some properties with
respect to the estimators of the parameters.
Key Words: Zero Inflated Model, With Zeros Model, Hurdle Model, Objective Prior, Objective
Bayes
On the Bayesian Estimation in Chain Binomial Epidemic Models
Manik Awale*
University of Pune, Pune, India
[email protected]
Mohan Kale
University of Pune, Pune, India
[email protected]
The aim of epidemic modelling is to understand the disease propagation and then to suggest
the control policies. Therefore to suggest best policies one needs to have the models which
capture the phenomenon of spread of disease under consideration accurately. Chain binomial
epidemic models are mainly used for the modelling of household epidemics. Various
researchers in late 70’s and 80’s studied the chain binomial epidemic models with respect to
common cold infection data, Tuberculosis infection data. However, Bayesian approach for the
analysis of such a peculiar data is rarely seen to the best of our knowledge. The present paper
deals with the estimation of parameters of the said models using Bayesian approach.
We have considered two variants of chain binomial models viz. Becker’s general chain binomial
model and Reed-Frost model. The parameters are estimated under squared error loss function
as well as linex loss function. The methods are evaluated using real life data on common cold
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and Tuberculosis available in the literature. Individual chain frequencies within household
infection are obtained for both the models using estimated parameters. The distribution of final
size of the epidemic is obtained using both models. Basic reproduction ratio for the epidemic is
also obtained for both the models. The consistency property of the estimators is established.
The proposed method is validated using simulation of individual chains and final size distribution
of the epidemic.
Key Words: Basic Reproduction Ratio, Chains of Infection, Final Size Distribution, Household
Epidemic, Loss Function.
Duality between Likelihood and Entropy in Bayesian Model Averaging
Toshio Ohnishi*
Kyushu University, Fukuoka, Japan
[email protected]
Takemi Yanagimoto
Chuo University, Tokyo, Japan
[email protected]
Maximization of the likelihood and that of the Shannon entropy are two of the most important
principles in statistical inference. This paper reveals notable duality between them in the
framework of Bayesian model averaging. We formulate a prediction problem, i.e., a density
estimation problem as a risk minimization under the -divergence loss, where  takes values
from –1 to +1. The +1-divergence is the Kullback-Leibler divergence from the predictor to the
true density, and the –1-divergence is the one from the true density to the predictor.
Considering a variety of losses is a key to highlighting the duality between the two principles.
Under the +1-divergence loss the following are obtained:
1) The risk minimization is equivalent to the Shannon entropy maximization under a constraint.
2) The likelihood maximization leads to the worst prediction among a class of nice predictors.
3) The log-likelihood ratio is derived as a quantity balancing with the +1-divergence loss.
Dually, under the –1-divergence loss, the following are obtained:
1) The risk minimization is equivalent to the likelihood maximization under a constraint.
2) The Shannon entropy maximization leads to the worst prediction among a class of nice
predictors;
3) The Shannon entropy difference is derived as a quantity balancing with the –1-divergence
loss.
Key Words: -Divergence, Convex Functional, Gateaux Derivative, Saddlepoint Equality, Semigroup.
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Exact Representation of Resampling Moments
Naoto Koshimizu*
Tokyo University of Science, Tokyo, Japan
[email protected]
Yoko Ono
Yokohama City University, Yokohama, Japan
[email protected]
Naoto Niki
Tokyo University of Science, Tokyo, Japan
[email protected]
Discussion is made upon a class of resampling methods, including Efron’s boot-strap and
Rubin’s Bayesian bootstrap, for estimating the sampling distribution of a statistic by sampling
weights allotted on the set of original observations, in case of small (tens, say) sample size,
where asymptotic evaluation of characteristics has inadequate accuracy. Shown in exact and
explicit but sometimes lengthy forms are the moments of the distribution of resampling moments
in terms of the moments of the weight distribution and the moments of the empirical distribution
using symmetric functions of several kinds. The moments of “moments of mo- ments” are also
given in terms of the population moments in place of those of the empirical distribution.
Numerical comparison between asymptotic and exact evaluations of characteristics for the
distributions of statistics are also made.
Key Words: Bootstrap, Bayesian Bootstrap, Small Sample, Symmetric Polynomial.
A Criterion on Apportionment Methods Minimizing the Rényi’s Divergence
Etsuo Kumagai
Osaka University, Osaka, Japan
[email protected]
In democratic states, seats are contested in the election. The seats are allocated by a rule
which is regulated by the Diet, the Congress, or the Parliament. In principle, their seats should
be proportional to the populations or the voters in their election districts, but it is difficult to
determine them exactly because the seats are integers and the ratios of populations are usually
rationals. To reduce the gap between them, many researchers have studied this in research
areas, such as, sociology, economics, operations research, and statistics. Ichimori (2012)
showed that apportionment methods maximizing the Rényi’s entropy are included in the divisor
methods and that his approach with the index is corresponding to the famous five
apportionment methods. It is, however, not clear which value of the index we should use among
the apportionment method maximizing the Rényi’s entropy. For the Rényi’s divergence with the
index, we propose a criterion with respect to the index on apportionment methods, which is the
sum of the Rényi’s divergence and a proposed function of the index as a kind of penalty.
Key Words: Apportionment Method, Rényi’s Divergence, Statistical Criterion.
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CPS06: OFFICIAL STATISTICS
Tax Statistics: A New Addition to the Official Statistics
Said Kamal Mohammed
Central Agency for Public Mobilization & Statistics (CAPMAS), Egypt
Email: [email protected]
Mob: 0020 01061135341
Taxes are the most important economic and financial tools by which the state can influence
economic life, in addition to other tools such as interest rates and wages. The goal of tax is not
only to get the largest possible financial revenue, but also to encourage the process of
economic and social development and the achievement of equity. Targeted from the local level
to the national economy and the level of each of the sectors and activities, taxes are also linked
to two national accounts analysis - investment and workforce and economic analysis. Many
questions can be answered through tax statistics. This paper aims to explain the importance of
producing tax statistics by following the methodology of tax laws to enable the government to
achieve a larger tax inventory.
Key Words: Economic Analysis, National Economy, Large Inventory Tax
Temporary Redistribution of Allocation Fund
Aulia Dini
STIS53 Economic Division Jakarta, Indonesia
[email protected]
Idaman
LiBelAIS Co. Ltd.
Jakarta, Indonesia
Since early in 3rd millennium central government has distributed allocation fund to both
provinces and regions (districts & big cities). Allocation fund is weighted by, among others, 14%
land area, 30% population size and three indices. If allocation fund is supposed to supplement
provincial and regional income then it will be more accurate to incorporate dependency ratio.
Next year to 2030 a temporary drop in dependency ratio is expected by most provinces and
regions. Some provinces and regions expect a temporary drop in dependency ratio next
decade. If 2013 dependency ratio is also taken into account then Eastern Nusa Tenggara at
68.3, Western Sulawesi at 61.6, Maluku at 61.1 need temporary increase of allocation fund.
Temporary increase in this case means greater than allocation fund obtained by application of
existing formula. Existing formula for allocation fund can be applied to remaining provinces.
Intuitively temporary increase of allocation fund is given only during temporary drop in
dependency ratio.
Other weights are: 14% Gross Domestic Product (GDP) and 15% inverse of Human
Development Index (HDI) and 27% Index of Physical Construction Cost (IKK). Public domain
data is usually available from government internet domain name (*.go.id). Available data for
2013 shows highest provincial IKK for Western Papua at 121.01, Papua at 188.7 and Northern
Maluku at 115.12. Since construction is supposed to directly affect productivity then these
provinces are in need of temporary increase of allocation fund. It is hopefully easy to understand
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that temporary increase of allocation fund can stimulate physical construction such as bridge,
road, clinic, school, worship place these high end values can show two different weights
compete for temporary redistribution of allocation fund. Our research is also applicable to
multiple weights (multi variate). For multi variate it is necessary to use an iterative process.
As of July 2014 our research for districts & big cities is not yet made available to general public
since 2013 dependency ratio for districts & big cities is not yet available as public domain. We
hope to be able to disseminate it before end of 2014
Key Words: Dependency Ratio, Allocation Fund, Physical Construction Cost
Using Principal Component Analysis in Index Construction: Application to Construction
Material Prices and Building Permit Applications
Kevin Carl P. Santos*
University of the Philippines, Diliman, Quezon City, Philippines
[email protected]
Generoso G. de Guzman
Philippine Statistical Research and Training Institute
Maria Praxedes R. Peña
Philippine Statistical Research and Training Institute
Novie Lyn S. Saladar
Civil Service Commission
This paper aims to generate a housing-related index which reflects not only the volatility in the
construction material wholesale prices but also the behavior of the housing demand with the
number of approved building permit applications as its proxy. This index may be used in
assessing the trends in asset prices and the risk of bubbles in the housing prices. The authors
utilized the data available from the Philippine Statistics Authority (PSA) from 2007 to 2012 and
integrated them with the Bill of Construction Materials from Subdivision and Housing
Developer’s Association (SHDA). Principal Component Analysis (PCA), a dimension reduction
statistical technique, was applied to linear combinations of the wholesale prices, number of
approved building permit applications, and bill of construction materials.
The authors also noted the caveats of the constructed index and challenges in data availability.
Key Words: Dimension Reduction, Housing-Related Index
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Quality of the Regulatory Governance and Affordability on Economic and Information
Seeking Use of Facebook in A Developing Country
Ewilly J.Y. Liew*
Department of Econometric and Business Statistics, School of Business, Monash University,
Sunway Campus, Malaysia
[email protected]
Santha Vaithilingam
Department of Econometric and Business Statistics, School of Business, Monash University,
Sunway Campus, Malaysia
Mahendhiran Nair
Department of Econometric and Business Statistics, School of Business, Monash University,
Sunway Campus, Malaysia
Facebook is the fastest growing social networking service and is seen as an important catalyst
for socioeconomic transformation across the globe (World Bank, 2012). Reaching the potential
of Facebook for socioeconomic development requires a careful understanding of key factors
that facilitate effective use of Facebook in the diffusion of information and knowledge. This study
explores the role of effective regulatory governance and affordability of physical infrastructure
on economic and information seeking use of Facebook in a developing country. One of the
major factors that contribute to high cost of physical Internet infrastructure in many developing
countries is the weak digital regulatory environment that leads to increased risks associated to
negative network externalities such as cybercrimes. Using a modified Technology Adoption
Model (TAM) on a sample of 367 Facebook users from Malaysia, this paper shows that effective
regulatory governance improves affordability of owning and using Internet for Facebook
services. The results also indicate that effective regulatory environment instils greater trust and
confidence for social interaction among Facebook users, thus increasing the perceived
usefulness and ease of using Facebook. Policy implications with respect to effective regulatory
governance and cost-effective measures for developing countries are discussed in the paper.
Key Words: Technology Adoption Model, Facebook, Affordability, Regulation
Big Data and Official Statistics in China
Yipin Xu*
National Bureau of Statistics, Beijing, China
[email protected]
As big data arisen, it is agreed that big data will have important influences on official statistics in
all countries of the world. Specifically, we realize that the coming of “Big Data” is not only a
significant opportunity, but also a challenge for official statistics in China. This paper discusses
the applications of big data in official statistics in China. The contribution of this paper is twofold.
First, I describe the current cases that big data is applied to some fields in Chinese government
statistics which include Consumer Price Statistics, Transportation Statistics, Real Estate
Statistics, Agriculture Statistics and using of Electronic Records in Investigation. Also, I
introduce some extensive cooperation between the NBS and big data enterprises. Second, I
propose some barriers we experienced when big data is applied. The barriers mainly reflect in
the following aspects: laws, regulations and data security, data openness, standards and
classifications of data, and information mining.
Key Words: Big Data Applications, Data Security, Openness of Data
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CPS07: STATISTICAL THEORY AND METHODOLOGY IV
A Note on Approximating ABC-MCMC Using Flexible Classifiers
Pham Thi Kim Cuc
National University of Singapore, Singapore 117546
[email protected] or [email protected]
David J. Nott
National University of Singapore, Singapore 117546
[email protected]
Sanjay Chaudhuri
National University of Singapore, Singapore 117546
[email protected]
A method for approximating Markov chain Monte Carlo algorithms is considered in a setting
where the likelihood is intractable. The approach is based on interpreting the likelihood ratio in
the Metropolis-Hastings acceptance probability as the odds in the Bayes classification rule for
distinguishing whether the observed data were generated using the proposal parameter value or
the current one. Approximating the Bayes rule using simulated data from the model and
modern flexible classifiers are capable of dealing with high-dimensional feature vectors, which
result in new approximated Bayesian computation (ABC) procedures that are able to perform
well with high-dimensional summary statistics. In handling problems of small to moderate sizes,
it may even be possible to dispense with summary statistics altogether. The synthetic likelihood
of Wood corresponds to the classification by quadratic discriminant analysis in this framework.
Key Words: Approximate Bayesian Computation, Bayesian Inference, Classification, Synthetic
Likelihood.
A Note on the Bivariate Generalized Linear Model with Identity Link Functions
Osama I. Idais
Currently, Department of Applied Statistics, East West University, Aftabnagar, Dhaka,
Bangladesh
[email protected]
M. Ataharul Islam
Department of Statistics and Operations Research, King Saud University, Riyadh, Saudi Arabia
Abdulhamid A. Alzaid
Currently, Department of Applied Statistics, East West University, Aftabnagar, Dhaka,
Bangladesh
Univariate generalized linear models (GLM) have been explored quite extensively during the
past three decades. However, there has not been much development in relation to the bivariate
or multivariate formulations of the GLM procedure with various link functions. To provide a clear
understanding of the procedure for bivariate generalized linear models, the model is illustrated
in this paper to identity link functions. The bivariate normal distribution is addressed in terms of
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the GLM methodology. The canonical parameters are specified as functions of covariates. The
likelihood method is used to obtain the estimates of the regression parameters. The deviance
function is developed as well.
Key Words: Bivariate Normal Distribution, Bivariate Generalized Linear Model, Score Function,
Deviance, Link Function
A weighted Approach to Zero-Inflated Poisson Regression Models with Missing Data in
Covariates
Martin Lukusa T.*
Feng Chia University
[email protected]
Shen-Ming Lee
Feng Chia University
Chin-Shang Li
Division of Biostatistics, Department of Public Health Sciences, UC Davis, USA
We consider a problem of overdispersion in count data where fitting a poisson model is likely to
produce inefficient and biased estimates. We propose instead to fit a Zero-Inflated Poisson
(ZIP) model. In addition to the overdispersion issue, covariates involved in modelling the
poisson mean and the mixing proportion may not be fully observed, that meaning that they may
have some missing values. Due to the cases deletion, the complete cases method (CC)
becomes biased and inefficient. We assume that the missings are at random (MAR).We
propose the Inverse Probability Weighting (IPW) where the selection probability is being
estimated nonparametrically. Furthermore, we derive some asymptotic properties and conduct
simulations study and real data.
Key Words: Zero-Inflated Poisson Models (ZIP), Missing at Random (MAR), Inverse Probability
Weighting (IPW), Large Sample Properties
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Measuring Explanatory Variable Contributions in Generalized Linear Models
Nobuoki Eshima*
Department of Biostatistics, Oita University, Oita, Japan
[email protected]
Claudio Giovanni Borroni
Dipartimento di Statistica e Metodi Quantitativi, dell.Universita di Milano-Bicocca, Milan, Italy
[email protected]
Minoru Tabata
Osaka Prefecture University, Osaka, Japan
[email protected]
Generalized linear models (GLMs) can be flexibly designed by random, systematic and link
components, and make useful regression analyses of both continuous and categorical response
variables. In GLMs except linear regression, only statistical estimation and testing of regression
parameters are performed in general; however the predictive or explanatory powers of GLMs
and explanatory variable contributions are not measured in many of practical data analyses.
Considering GLMs in view of entropy, the entropy coefficient of determination (ECD) (Eshima &
Tabata, 2010, 2011) have been proposed, which is an extension of R2 in the ordinary linear
regression model and can be applied to all GLMs. The explanatory variable contributions have
not been discussed for GLMs with categorical response variables. The objective of the present
paper is to discuss the explanatory variable contributions in GLMs. First, we make a preliminary
discussion on the topic by using a linear regression model. Second, the entropy coefficient of
determination (ECD) is reviewed. Third, we propose an ECD approach to assessing the variable
contributions in GLMs. Finally, the present approach is illustrated by using a numerical example.
Key Words: Categorical Response Variable, Entropy Coefficient of Determination, Variable
Contribution, Predictive Power.
Estimation of Concordance Statistics for Logistic Regression Models: A Simulation
Study
M. Shafiqur Rahman, PhD*
Institute of Statistical Research and Training, University of Dhaka, Bangladesh
[email protected]
Biplob Biswas, MSc
Institute of Statistical Research and Training, University of Dhaka, Bangladesh
Logistic regression models are frequently used in various settings of clinical research to predict
the risk of a patient's future health status such as death or illness using his/her clinical and
demographic characteristics. Predictions based on these models have an important role in
classifying the patients with low-and high-risk and hence in guiding their future courses of
treatment. Given their important role in clinical research, it is very essential to evaluate the
predictive performance of the model e.g. the ability of the model to distinguish between low- and
high-risk patients-which is termed as `discrimination'. Concordance statistic (C-statistic), which
is equivalent to the area under a receiver operating characteristic curve (AUC), is frequently
used to quantify the discriminatory power of the logistic model because of its straightforward
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clinical interpretation. Several methods for estimating concordance statistics including nonparametric and parametric has been proposed in the literature. Despite the proposal of several
estimation approaches for C-statistic, it is still unclear to the practical users which approaches
should be applied in practice. This paper evaluated some methods of estimating C-statistic by
an extensive simulation study and compared the results in order to make some practical
recommendations. Several simulation scenarios were considered by varying the sample size,
prevalence and distribution of prognostic index (or log-odds) derived from the model, to evaluate
their (concordance estimators) bias, mean squared error (MSE) and sensitivity to the increment
in the model predictive performance. The results showed that the non-parametric method
performed better in most of the simulation scenarios compared to the other methods based on
Kernel smoothing and parametric approach. An application of the methods is provided to the
Bangladesh child mortality data.
Key Words: Logistic Regression, Prediction, Discrimination, and Concordance Statistics.
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CPS08: STATISTICAL APPLICATIONS I
The Equilibrium Distribution of The Hwang-Green Substitution Model On A Circular DNA
Yap Von Bing
Department of Statistics and Applied Probability
National University of Singapore
[email protected]
The DNA substitution process has been studied intensely for a few decades. The simplest
model is a Markov chain on the nucleotides {A, C, G, T} that acts independently on a DNA
sequence. However, there are some known dependence of substitution rates on neighbouring
nucleotides. The Hwang-Green model allows substitution rates to depend on the two nearest
neighbours, hence has up to 12 x 4 x 4 = 192 parameters. For simplicity, consider a circular
DNA of length n. The state space has 4^n elements. We will discuss several questions on the
equilibrium distribution of this Markov chain, along the line suggested by Zhang and Yap (2013).
On The Estimation of Survival of HIV/Aids Patients on Anti-Retroviral Therapy: An
Application to Interval Censored Data.
Prafulla Kumar Swain*
Department of Statistics, Faculty of Mathematical Sciences, University of Delhi, Delhi, India
[email protected]
Gurprit Grover
Department of Statistics, Faculty of Mathematical Sciences, University of Delhi, Delhi, India
The main objective of this paper is to estimate the survival of HIV/AIDS patients who are
undergoing Antiretroviral Therapy treatment in an ART centre, Delhi, India. Non Parametric
Maximum Likelihood Estimation NPMLE (E-M) for interval censoring and KM survival plot for
left, right and mid-point imputation have been used to estimate the survival of these patients. It
has been observed that that the mid-point imputed survival plot has a very similar and
consistent pattern as obtained by NPMLE (E-M) method. Considering these mid-point imputed
values as right censored data, Cox PH model and Accelerated Failure time Model (AFTM) have
been applied to study the effects of prognostic factors like age, sex, mode of transmission,
baseline CD4 cell count, hemoglobin, baseline weight and smoking habits on the survival of the
patients. The Akaike Information Criterion (AIC) has been employed to compare the efficiency of
the models and Cox-Snell residual to test proportionality assumption.
Key Words: AIDS, ART, CD4 cell count, Non Parametric Maximum Likelihood Estimation, CoxPH Model, Accelerated Failure Time Model
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A Statistical Model for Pre-Post Studies with Continuous Bounded Outcome Scores
Tetsuji Ohyama*
Oita University Faculty of Medicine, Oita, Japan 879-5593
[email protected]
Yuki Suzaki
Osaka University Hospital, Osaka, Japan 565-0871
Nobuoki Eshima
Oita University Faculty of Medicine, Oita, Japan 879-5593
We propose a statistical model for analyzing the effects of factors and covariates in pre-post
studies with continuous bounded outcome scores (BOSs). BOS is a random variable restricted
to a finite interval, and often shows non-normality and/or heteroscedasticity. Nonparametric
methods are motivated, but they are less powerful if parametric assumptions are met. Although
transforming the scores is also discussed to treat BOS data, it cannot necessarily reduce the
non-normality and heteroscedasticity at the same time. Even if it can, complex transformations
might lead to the difficulty of interpretation in the original scale. To overcome the above
problems, a bivariate beta regression model is proposed to assess the effects of factors and
covariates on continuous BOSs in the present context. In the model construction, a random
component is a bivariate beta distribution. Systematic components are linear functions of factors
and covariates, and mean and dispersion parameters of the bivariate beta distribution are linked
to the systematic components. The present approach is applied to statistical analysis of a
clinical research data.
Key Words: Beta Regression, Bivariate Beta Distribution, Gamma Distribution, Visual Analog
Scale
Multivariate Approach to Modelling Clinical Trials Data
Liz Stojanovski
University of Newcastle, Australia
[email protected]
Clinical trials often involve the collection of data comprising multiple dimensions of similar
measures which are often highly correlated. Standard approaches often involve assessing each
measure separately in each model which ignores the information contained in the other, often,
highly correlated measures. Such measures include outcome data collected from cancer
clinical trials.
Our objective is to model simultaneously data collected from a study assessing various aspects
of clinical trials in cancer patients. The methodology utilizes multiple measures simultaneously
and accommodates a common concern of missing items and alleviates the need to use only
selected outcome measures via a latent modelling approach.
Key Words: Clinical Trials, Multivariate Modelling, Latent
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CPS09: ECONOMICS AND FINANCE
Network Analysis of Currency Exchange Market: A Multivariate Time Series Approach
Mansooreh Kazemilari
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia,
Skudai, Johor
[email protected], or [email protected]
Maman Abdurachman Djauhari
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia,
Skudai, Johor
This paper shows how the concept of Escoufier's vector correlation can appropriately measure
the similarity among multivariate time series in currency network.
The motivation of this article is:
i)
To apply the RV coefficient to define the network among currencies where each of them
is represented by a multivariate time series.
ii)
To analyze that network in terms of topological structure and economic classification of
the currencies of all minimum spanning trees, forest and sub dominant ultra-metric.
Key Words: Network, multivariate, currencies
Economic Sectors Network at Kuala Lumpur Stock Exchange
Gan Siew Lee*
Universiti Teknologi Malaysia, Johor Bahru, Malaysia
[email protected]
Maman A. Djauhari
Universitas Pasundan, Bandung, Indonesia
[email protected]
Zuhaimy Ismail
Universiti Teknologi Malaysia, Johor Bahru, Malaysia
[email protected]
The relationship between stocks in a portfolio has been widely studied by using network
analysis approaches. The minimal spanning tree is an indispensible tool to analyze the
topological properties of stocks. However, there is lack of studies in dealing with the network
topology of economic sectors in a portfolio. Each economic sector is composed of a group of
stocks that have similar economic activities. Mathematically, the relationship between economic
sectors is about the similarity of two multivariate time series which might be of different
dimensions. It means that the network analysis among economic sectors is in multivariate time
series setting. In this paper, we consider each sector as an element on the financial system and
construct the minimum spanning tree by using the vector correlation matrix for the sectors. As
emerging market, there is a lack of studies focused on the topological properties of Malaysian
stock market. This paper focuses on the topological properties of Malaysian stock market at
sector level. Some important results will be highlighted.
Key Words: Complex Network, Correlation Coefficient, Econophysics, RV Coefficient
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Reaction of Interest Rate to Inflation Gap, Output Gap and Exchange Rate: Evidence from
Asean Countries
Wai Ching Poon
Department of Economics, School of Business, Monash University Malaysia
[email protected]
We test whether interest rate reacts to inflation gap, output gap and real exchange rate in
ASEAN-10 (three are inflation targeters (IT) and seven are non-targeters) from 1990 to 2010
using dynamic panel approaches and pooled estimates. Results exhibit that both IT and non-IT
responses to inflation gap, output gap and lagged interest rate in setting interest rate movement,
with larger weight on output gap compared to inflation gap. Output gap carries more information
than inflation gap in setting interest rates for IT and non-IT. This result lends support to the
literature that the real term is more important that the nominal term. Real exchange rate appears
as a weaker key determinant in setting interest rates for non-targeters than the targeters.
Key Words: Inflation Targeting, Real Exchange Rate, ASEAN Countries
Effects of Climate Shocks to Philippine International Trade
Mark C. Pascasio*
Philippine Statistics Authority, Makati, Philippines
[email protected]
Shingo Takahashi
International University of Japan, Niigata, Japan
[email protected]
Koji Kotani
Kochi University of Technology, Kochi, Japan
[email protected]
As climate change is established to occur on scientific bases, it is imperative to identify the
effect of climate shocks on the economy. According to international organizations, agriculture,
forestry and fisheries are the sectors most vulnerable to climate change predominantly for
developing and tropical countries, and thus it is hypothesized to have significant impact on
world-wide international trade. Although Jones and Olken (2010) demonstrate the effect of
climate shocks on exports using U.S. and world data, the evidence is still highly scarce for
developing countries. Given these conditions, we examine how climate shocks affect
international trade by focusing on the case of the Philippines as a representative of developing
and tropical countries. To this end, we apply a fixed effects model using the data of Philippine
international trade and world climate from 1991 to 2009. In particular, the novelty lies in
examining both exports and imports within a single empirical framework and in clarifying climate
shocks on both flows of international trade. The results show that both Philippine exports and
imports are negatively affected by an increase in temperature of its trade partners. We have
also identified some specific sectors are highly vulnerable such as agriculture and
manufacturing. Overall, these results imply that Philippine international trade shrinks as the
world temperature rises, and the same qualitative results may apply to other developing and
tropical countries whose features are somewhat similar to those of the Philippines. The findings
could be considered an important guidance on collective policy decisions on climate change in
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an international community as developing and tropical countries would have difficulties in
mitigating the effect only by themselves.
Key Words: Climate Change and Shock, Temperature, Philippine International Trade
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CPS10: STATISTICAL APPLICATIONS II
Risk-Adjusted Cumulative Sum Charting Procedure Based on Multi-Responses
Tang Xu
Department of Statistics and Applied Probability, National University of Singapore, Singapore
[email protected]
Gan Fah Fatt
Department of Statistics and Applied Probability, National University of Singapore, Singapore
[email protected]
Zhang Lingyun
Formerly of BNU-HKBU United International College, Zhuhai, China
[email protected]
The cumulative sum charting procedure is traditionally used in the manufacturing industry for
monitoring the quality of products. Recently, it has been developed for monitoring surgical
outcomes. Unlike a manufacturing process where the raw material is usually reasonably
homogeneous, patients' risks of surgical failure are usually different. It has been proposed in the
literature that the binary outcomes from a surgical procedure be adjusted using the preoperative
risk based on a likelihood ratio scoring method. Such a crude classification of surgical outcome
is naive. It is unreasonable to regard a patient who has a full recovery, the same quality
outcome as another patient who survived but remained bed-ridden for life. For a patient who
survives an operation, there can be many different grades of recovery. Thus, it makes sense to
consider a risk-adjusted cumulative sum charting procedure based on more than two outcomes
to better monitor surgical performance. In this paper, we develop such a chart and study its
performance.
Key Words: Collocation Method, Odds Ratio, Parsonnet Scores, Proportional Odds Logistic
Regression, Quality Monitoring, Surgical Outcomes.
Generalized Variance Chart: Root Causes Analysis
Revathi Sagadavan*
Universiti Teknologi Malaysia, Johor, Malaysia
[email protected]
Maman A.Djauhari
Institute for Mathematical Research, Universiti Putra Malaysia Selangor, Malaysia
[email protected]
Ismail Mohamed
Universiti Teknologi Malaysia, Johor, Malaysia
Process variability monitoring is an essential part of any process, where more than one quality
characteristics are correlated and need to be handled simultaneously. In the literature, some
control charts are available to do so. Generalized variance (GV) chart is the most commonly
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used because of its commendable geometric properties and computationally efficient. However,
despite its practical popularity, the problem is still open to identify the root causes when this
chart gives an out-of-control signal. In this paper, we propose a method to identify which
variables are significantly responsible to the occurrence of that signal. For this purpose, the GV
is factorized into individual conditional variances and then use Chi Square test for the ratio of
conditional variances to find which variables are significantly responsible to an out-of-control
signal. An industrial example is presentedto illustrate the advantage of the proposed method.
Key Words: Chi- Square Test, Partition of Covariance, Out of Control, Process Variability
On the Use of Multivariate Control Charts for Monitoring Television Ratings
Genelyn Ma. F. Sarte*
School of Statistics, University of the Philippines, Diliman, PHILIPPINES
[email protected] or [email protected]
Jasper P. Tamargo
a) School of Statistics, University of the Philippines, Diliman, PHILIPPINES
b) Kantar Media Singapore, SINGAPORE
Television (TV) ratings are regularly monitored by networks and advertisers because these
serve as measures to determine how well different programs are received by the audience. The
data generated from TV ratings are naturally multivariate – the ratings produce autocorrelated
data for each program over time and correlated data across different networks for the same time
slot. While it is possible to create control charts for TV ratings of competing programs
individually, this can be misleading since the ratings are correlated. This paper explores the use
of Hotelling T2 control chart and the Principal Component (PC) – based control chart using
weekday daily TV ratings in the Philippines from January 1, 2008 to November 30, 2011 for the
1801h to 2400h time band. As expected, results show that what appears as out-of-control
points in the univariate control charts do not necessarily appear as out-of-control points in the
multivariate control charts. Likewise, multivariate control charts show that the impact of a new
show introduced in a Philippine TV station within the time band is short-lived as opposed to the
seemingly long-term impact that the univariate control charts suggest.
Key Words: Hotelling T2 Control Chart, Principal Component-Based Control Chart
Power Analysis for Detecting Interaction Effects in Heteroscedastic Factorial Designs
Gwowen Shieh*
National Chiao Tung University, Hsinchu, Taiwan
[email protected]
Show-Li Jan
Chung Yuan Christian University, Chungli, Taiwan
[email protected]
Several viable approaches have been proposed for conducting factorial analysis when the
homogeneous variances assumption is violated. Although robust Type I error control and
excellent power performance are desirable properties of a test procedure for making statistical
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inferences, the corresponding power calculations must also be considered to extend its
applicability in planning research studies. This article presents the power function of
approximate degrees of freedom test within the context of two-way factorial designs. Simulation
results showed that the suggested procedure provide remarkably good approximation over a
wide range of model configurations.
Key Words: Analysis of Variance, Contrast Analysis, Heterogeneity
Autocorrelated Process Control: A Geometric Brownian Motion Approach
Lee Siaw Li*
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310
UTM Skudai, Johor Bahru, Malaysia
[email protected]
Maman Abdurachman Djauhari
Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia
[email protected]
Ismail Mohamad
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310
UTM Skudai, Johor Bahru, Malaysia
[email protected]
The current practice in an autocorrelated process control is to fit a time series model to the
process data and then to apply the control chart to the residuals. It has been shown that if the
process is governed by the geometric Brownian motion (GBM) law or, equivalently, the
logarithmic returns is AR(1) process, the fitted model building needs neither model identification
nor verification. It contributes to a simple modeling process. An industrial example suggests
considering the GBM modeling as a counterpart of the ARIMA modeling.
Key Words: Autocorrelation, Residuals, Shewhart Control Chart, Statistical Process Control,
Time Series Modeling.
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CPS11: STATISTICS IN SOCIAL SCIENCES I
Statistics Education Research in Malaysia and the Philippines: A Comparative Analysis
for Future Directions
Enriqueta Reston*
University of San Carlos, Philippines
[email protected]
Saras Krishnan
University of Malaya, Malaysia
[email protected]
Noraini Idris
University of Malaya, Malaysia
[email protected]
This paper presents a comparative analysis of statistics education research in Malaysia and the
Philippines by modes of dissemination, research areas and trends. An electronic search for
published research papers in the area of statistics education from 2000-2012 yielded 20 and 19
for Malaysia and the Philippines, respectively. Analysis of these papers showed that most were
primarily empirical research published in local refereed journals or in conference proceedings.
Statistics education research in Malaysia has focused on integration of technology and on
affective aspects of statistics learning. In the Philippines, studies have investigated universitylevel statistics pedagogy, statistics academic programs and teachers’ professional development.
Implications for future statistics education research and teaching practice in these two countries
are identified.
Key Words: Statistics Education Research, Philippines, Malaysia, Comparative Analysis
Learning Points in Producing Business Statistics as International Accounting Standard
Adopts
Park, Youn-Young
International Cooperation Division, Statistics Korea, Daejeon, Republic of Korea, 302-701
[email protected]
The importance of business accounting has increased as it constitutes core information in the
decision making process of various individuals. However, the production environment for
business accounting is increasingly complex and diversified due to the emergence of many
challenging issues. Recognising the complexities and importance of business accounting as an
component of official statistics, this paper will aim to find out the main issues and implication of
new international accounting standards on business accounting. The main topic of this paper is
to review business combination, consolidation, and other consolidation issues. The paper also
examines the learning points from new accounting standard issues. The final section of the
paper concludes and recommends appropriate strategies to cope with the new environment
arising from the introduction of new accounting standards as an infrastructure of official
statistics.
Key Words: IFRS, Business Statistics, Businesses Combination, Consolidation
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Forecasting Admissions of Comsats Institute of Information Technology (CIIT)
Erum Rehman,
Comsats Institute of Information Technology, Islamabad, Pakistan
[email protected],
Dr.Noor.M.Larik,
Comsats Institute of Information Technology, Islamabad, Pakistan
[email protected],
The increasing competition for admissions in Higher Education Institutes for admissions has
become a problem for Higher Education Institutes for making adequate policies for decision
making. The COMSATS Institute of Information Technology has eight campuses all over the
country and headquarters in Islamabad, Pakistan. Due to its popularity and ranking first in the
country as institute of science and technology and sixth in all general universities of country.
There has been general significant increase of the students for admissions. Thus it requires
future demand for admissions to make adequate policies for students as well as faculty
requirement. In this article, an attempt has been made to forecast admissions of students
particularly in main campus located in Islamabad. By using models such as Holts linear trend
model, simple linear regression models and simple linear model, it has been concluded that
simple linear regression model gives us more accurate results as compared to others.
Application of Binary Logistic Regression on Undergraduate Result Data (A Case Study
of Abia State Polytechnic, Aba, Abia State Nigeria)
Nwobi anderson Chukwukailo
Department of Statistics, Abia State Polytechnic, Aba Nigeria
[email protected]
GSM: +2348036018752
In this paper, the study examines the school examination results (scores) of 330 randomly
selected students of Abia State Polytechnic, Aba, Abia State, Nigeria which offers General
English Language and Mathematics courses, using the binary logistic regression model with the
aim of studying how some factors (variables) in secondary school level contribute to the
performance of the students in the higher institution. The analysis however is performed on the
basis of the independent variables viz; class of family, gender, type of secondary schools,
category of secondary schools, board of examinations and location of secondary schools, where
scores of students in general English Language and Mathematics are assumed to be the
dependent variables. Primary data were used to gather the information used for this research
work. Method of Correspondence Analysis showed that there exist a significant correlation
between gender of students and the location of schools; the board of examinations and location
of schools. This resulted in an analysis involving three stages. The result of the analysis
revealed that class of family significantly affected the results of students in the three stages,
apart from the first and the third stage, where it does not affect the result of students in the
General English Language course.
Key Words: Odds Ratio, Wald Statistics, Logistic Regression Model, Correspondence Analysis
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Relationship between Self-efficacy and Quality of Life: Female Teachers in Sarikei,
Sarawak
Zanariah Ismail
Department of Human Development and Family Studies, Faculty of Human Ecology, Universiti
Putra Malaysia, Serdang Selangor, Malaysia
[email protected]
Lei Li Shien
Department of Human Development and Family Studies, Faculty of Human Ecology, Universiti
Putra Malaysia, Serdang Selangor, Malaysia
[email protected]
Recently, the Malaysian government makes a huge effort in changing our education system
such as the introduction of new subjects in primary and secondary schools and teachers are
obligated to sign a good conduct agreement which monitors teacher performance in classroom
and other changes which burden teacher in their daily teaching routines. This study examines
the relationship of self-efficacy and quality of life among female secondary school teachers in
Sarawak. The study addresses two research questions: Is there any relationship between selfefficacy and quality of life of female secondary school teachers? Secondly, what are the unique
predictors (age, years of teaching experience, monthly income, self-efficacy, stress and coping
strategies) of quality of life among female secondary school teachers in Sarikei, Sarawak? The
finding shows that quality of life was associated with an increased of teachers’ self-efficacy. In
addition, stress was the strongest predictors for quality of life of female secondary school
teachers in Sarikei, Sarawak. Thus, it is important for the teachers to manage stress in order to
improve their quality of life. This finding also support that self-efficacy motivates teachers to
maintain persistence towards their career.
Further discussion on the theoretical,
methodological, and implications were also explained.
Key Words: Stress, Coping Strategies and Predictors
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CPS12: STATISTICAL APPLICATIONS III
A Regionalized Approach in Modeling Tropical Rainfall Process using Gamma
Distribution
Norzaida Abas, Zalina M. Daud
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia
Kuala Lumpur Campus, Jln Semarak 54100, Kuala Lumpur, Malaysia
[email protected]
Zalina M. Daud
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia
Kuala Lumpur Campus, Jln Semarak 54100, Kuala Lumpur, Malaysia
Siti Musliha Mat Rasid
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia
Kuala Lumpur Campus, Jln Semarak 54100, Kuala Lumpur, Malaysia
In the tropics, high rainfall variability in time and space has resulted in different rainfall
distribution over a region. In particular, Peninsular Malaysia could be divided into two distinct
rainfall sub-regions. Past studies on Malaysian rainfall showed that most rainfall models were
developed within a homogeneous region. In addition, such models focused on producing
synthetic rainfall series at coarse resolution i.e daily or monthly scale. However, in this study, a
stochastic space time rainfall model based on the Neyman Scott process is developed to model
rainfall process at hourly scale using regionalized approach. Neyman Scott model is defined by
three independent stochastic processes; storm origin, number of rain cells generated by each
storm and cell origin. The study employs Gamma distribution to represent intensity of rain cell.
Regionalization process involved the amalgamation of hourly and daily historical data from
multiple rainfall stations all over the peninsula to account for spatial variation between sites. To
ensure spatial variability is minimized, these data are priorly scaled according to the local hourly
mean. Statistical characteristics used for model development include hourly and daily
coefficient of variation, hourly and daily lag one auto correlation, hourly cross correlation
between sites and hourly skewness of rainfall series. Model’s performance is evaluated by
comparing generated rainfall series to the observed data at an independent site. Results show
statistical characteristics of simulated series matched the observed series fairly well.
Considering that space time rainfall variability is high within the peninsula, this model is able to
produce reasonable synthetic hourly rainfall series which captured the characteristics and the
pattern of rainfall series.
Key Words: Gamma Distribution, Regionalized Approach, Space Time Model, Tropical Rainfall.
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Daily Rainfall Modelling Using Zero-Inflated Gamma
Nanda Rizqia Pradana Ratnasari*
Brawijaya University, Malang, Indonesia
[email protected]
Solimun
Brawijaya University, Malang, Indonesia
[email protected]
Suci Astutik
Brawijaya University, Malang, Indonesia
[email protected]
Daily rainfall data often contain many zero values, usually called intermittent data. The
intermittent data that accommodate zero value and continue distribution data called
semicontinues data. The technique to analyze data with many zeroes is often done in a
mixture-model or two-part models. The mixture model discussed in this research is a model
that combine logistic and gamma, called zero-inflated gamma (ZIG). The contributions of this
research are the modeling of daily rainfall using ZIG and the prediction of the probability of
rainfall. The rainfall data in this paper are obtained from Sentral Station of Sampean
Watershed, Bondowoso, Indonesia for January 2014 period. To estimate the parameter, we
use Maximum-Likelihood Method based on Newton-Raphson iteration. The validity of the model
can be seen from the model performance based on the accuracy of the prediction and
observations. Finally, the model can be used to forecast the probability and amount of rainfall.
Thereby giving early information about the occurrence of flood.
Key Words: Semicontinues Data, ZIG (Zero-Inflated Gamma), Daily Rainfall
Statistical Method for Examining Temperature Variation in South Asia from 1973 to 2013
Cherdchai Me-ead*
Prince of Songkla University, Pattani, Thailand
[email protected]
Nittaya McNeil
Prince of Songkla University, Pattani, Thailand
[email protected]
The aim of this study is to investigate trends and patterns of monthly temperature in South Asia.
The data were obtained from the climate research unit from 1973 to 2013 comprising 25 regions
of 10o by 10o grid-boxes in latitudes 35oN to15oS and longitudes 45oE to 100oE. The data was
filtered with a second order autoregressive process to remove autocorrelation between
temperature lags. The factor analysis was used to classify monthly average temperature
anomalies into 3 larger regions by considering the relationshIS between its adjoining regions.
Simple linear regression models were then fitted to the data of 3 larger regions. The result
showed that the temperature in these 3 larger regions has increased from 0.136 to 0.309 per
decade.
Key Words: Linear Regression Model, Autocorrelation, Factor Analysis, Time Series Analysis
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Fitting Daily Rainfall Amount in Thailand Using Gamma Regression
Mayuening Eso*
Prince of Songkla University, Pattani, Thailand
[email protected]
Metta Kuning
Prince of Songkla University, Pattani, Thailand
[email protected]
The objective of this study is to develop an appropriate generalized linear model of daily rainfall
during 2001-2012 in Thailand. In this study, serial correlations are removed by restructuring the
data as 5-day means, because conventional statistical models assume independent errors. In
multivariate analysis, factor analysis is used to reduce a large numbers of stations. It is found
that seven regions with similar patterns of daily rainfall in Thailand are the upper north, lower
north, north-east, central, south-west, upper south and south-east. Gamma regression is fitted
in the period of rainy season for each region.
Key Words: Daily Rainfall in Thailand, Factor Analysis, Generalized Linear Model, GLM
Confidence Bands for Confidence Intervals from Data of Two Parameters Exponential
Distribution under Complete Censoring (Study Case: Waiting Time of Earthquake
Disasters in Indonesia at March 2013)
Puteri Pekerti Wulandari
Students of Statistics
Universitas Islam Indonesia, Yogyakarta, Indonesia
[email protected]
Akhmad Fauzy
Lecturer of Statistics
Universitas Islam Indonesia, Yogyakarta, Indonesia
[email protected]
Earthquakes are one of the frequent disasters in Indonesia. Based on data from the National
Disaster Mitigation Institution (BNPB) recorded in March 2013 has occurred 7 times earthquake.
This research was aims to find confidence bands for confidence interval from data of
exponential distribution two parameters under complete censoring.
The results of the data analysis showed that the earthquake in March 2013 has been distributed
exponential with time interval earthquake, the fastest is 1 day and the longest was 7 days. The
average waiting time for an earthquake parameter μ is 1 day and the parameter θ is 3 day. For
the average waiting time of 1 day have a probability 86.7% and the average to waiting time of 7
days have a probability 14.3%. From the plot it can be seen that the resulting confidence band
moves decreases exponentially.
Key Words: Exponential, Estimates, Earthquake, Intervals, The Complete Sensor
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ISI-RSC 2014 ABSTRACT BOOK
CPS13: STATISTICS IN SOCIAL SCIENCES II
Electoral Sociology - Egypt 2011-2013
Hala Bayoumi*
CEDEJ/CNRS, Cairo/ Paris, Egypt/France
[email protected]
This work represents the formulation of a new hypothesis on the sociology of voting in Egypt
between 2011 and 2013. An explanation of the designed research methodology, which was
used to fulfill the research goal, will also be provided in the paper. Over the course of two years,
a substantial amount of electoral data was collected and analyzed on different territorial scales
to be studied against our previously built socio-economic database of Egypt. The main goal of
this research was to create the foundation of the electoral sociology of Egypt. Since the socioeconomic database of Egypt was absolutely exhaustive, the database of the Egyptian electoral
results had to be equally comprehensive and exhaustive. The collection of the results of the
legislative elections in 2011 was conducted over the 45 electoral constituencies of Egypt.
Correspondingly, the results of the presidential elections and re-elections in 2012 were also
collected and studied from all the 395 administrative divisions of Egypt, Markaz and Qism. A
mathematical treatment was then used with the primary objective of revealing the electoral
patterns of the voters, in relation to the different variables. The process of the quantitative
analysis of the data are as follows:
 A multi-dimensional description analysis
 A Structural analysis was then made to classify the data
 Then we validated the data through linear regressions
 Finally we made our correlations
Statistical results demonstrate a better understanding of electoral sociology.
Key Words: Elections, Correlation, Poverty, Modeling
Migration Survey 2013 in the Middle East: A Comparison Study
Nehall Ahmed Farouk Mohamed
Research, Sampling and IT Specialist
Central Agency for Public Mobilization and Statistics (CAPMAS), Cairo, Egypt
[email protected]
Egypt conducts many important surveys such as economic census, labor force survey,
household income expenditure consumption survey (HIECS), and international migration. The
international migration survey was recently conducted in Egypt and in other two countries in the
Middle East (Morocco and Jordan) in 2013 for three migration categories: current migrant,
returned migrant, and non-migrant. The paper shows the migration survey which was designed
to overcome the lack of data on international migration for the region through the collection of
reliable and representative multi topic, multi-level, retrospective and comparative data on the
characteristics and behavior of migrants and the determinants and consequences of
international migration and mobility. The sampling plan and design for the migration survey in
Egypt uses the nationally representative Master Sample (MS) recently updated in 2010,
covering 5024 enumeration areas (EAs), making it appropriate to be used as a sampling frame.
The Egypt–Master sample (MS) was based on a sample of 1000 Primary Sampling Units
(PSUs). Finally we compare the sampling frame, size, and design in the Middle East survey
countries (Morocco–Jordan–Egypt).
Key Words: Master Sample of Egypt, Stata, R as Sampling Software, Sampling Errors, Global
or Local Estimates.
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Woman Participation in Labor Force In Upper Egypt
Reem Ismail Mohamed Elsybaey
Central Agency for Public Mobilization and Statistics (CAPMAS), Cairo-Egypt
[email protected]
In the last two decades, the world has showed an interest in the role of women in family and
society as a participant in development alongside men. The participation of Egyptian women in
the labor force is an important issue. Despite the efforts that have been made in this framework,
there are many challenges that negatively impact the ability of Egyptian women to actively
participate in economic life. The most important of these constraints is the low participation of
women in the labor force, high unemployment rate among females compared to males where
the rate among women is about 24% more than the rate among men which is about 6.8%.
According to many studies there exists a relationship between women's participation in the labor
force and other determinants like woman's age, level of education for both spouses, place of
residence, and number of children of a working women and a lot of other factors that would
increase or reduce the likelihood of women's participation in the labor force. The labor force
survey which is implemented by the Central Agency for Public Mobilization and Statistics
(CAPMAS) provides data about work status for women as well as data on demographic, social
and economic characteristics. This paper will review the status of Upper Egyptian woman
according to many determinants such as, level of education, marital status and her work status.
It also applies the logistic regression model to the factors affecting Woman’s participation in
Labor Force in Upper Egypt.
Key Words: Logistic Regression, Working Woman
Economic Assimilation of Rural-Urban Migrants: Evidence from Indonesia
Senadheerage Pamudi Banjitha Abeynayake*
Monash University Malaysia, Selangor, Malaysia
[email protected]
Internal migration is a common livelihood strategy undertaken by the poor within a nation in an
attempt to improve their economic standing. However, it is argued that many get trapped in low
paying informal sector employment. To date many studies have investigated whether migrants
improve their income upon migration. In the Indonesian context, research suggests that internal
migrants earn a higher wage than urban non-migrants. However, observing wages over time is
insufficient to understand the complete situation of migrants in the labour market. The present
study is an attempt to provide a better understanding about migrants’ economic assimilation by
examining the total compensation package (monthly salary, value of in-kind benefits) and using
a new definition of what constitutes of informal employment. For this purpose, data on
Indonesian internal migrants and non-migrants from the Rural-Urban Migration in Indonesia
(RUMiI) Project from year 2008 is used to gain insights into the fragmented nature of the urban
labour market in the four Indonesian cities included in the dataset. Initial findings from Ordinary
Least Square (OLS) regression analysis suggest that migrants receive better compensation
packages in comparison to their urban counterparts. Moreover, it appears that in Indonesia the
extent of job informality is an important determinant of the value of the remuneration package
received by workers, but non-migrants are penalised to a greater extent than migrants for
working in informal employment. Thus, it appears migrants outperform the urban natives.
Key Words: Internal Migrants, Informality, Remuneration, Working Conditions
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Estimation of Cause Specific Death Rates for India by Data Triangulation
Akansha Singh*
International Institute for Population Sciences, Mumbai, India-400088
Email: [email protected]
Laishram Ladusingh
International Institute for Population Sciences, Mumbai, India-400088
Email: [email protected]
Reliable data of deaths by cause is essential for planning, managing and monitoring
epidemiological transition and improvement of public health system in all nations. Keeping this
in view, in India the scheme of Medical Certification of Cause of Death (MCCD) was introduced
under the provisions of Registration of Births and Deaths Act, 1969. However, deaths covered
under MCCD scheme is far from complete and are largely confined to selected urban settings.
As such MCCD data do not represent the general population. Indirect techniques for estimating
cause-of-death patterns can be useful to overcome the paucity of cause of death statistics. This
study is an attempt to estimate cause specific death rate due to major cause groups by data
triangulation and using indirect estimation techniques. The major data sources used in this
study are MCCD, Sample Registration System and other government reports for the year 1990
to 2008. The cause specific death rate for the selected states with good coverage were
estimated. Linear regression models based on the cause of death modelling approach was
used to establish the relationship between the cause specific death rate for nine broad causes
of death groups with all cause death rate and other predictor variables. The regression
coefficients estimated from these models were used to estimate cause specific death rate for
the major states of India. The outcomes indicate that the nine models for the broad cause of
death groups are giving a good fit to the data and their regression coefficients can be applied for
estimation of cause of death in the other states for the time period of 1991-95 to 2006-10. The
adjusted R2 values are as high as 0.8 for five cause of death models. The outcomes for the
major states of India show that the death rate due to infectious and parasitic diseases, diseases
of the nervous systems and sense organs, diseases of the digestive system, certain conditions
originating in the perinatal period and other causes has declined to a great extent in India. The
death rate for neoplasm, diseases of the circulatory system, and diseases of the respiratory
system has shown a steady increase over the period of time. The increase has been more
significant in the recent time period from 2001-05 to 2006-10. Considerable interstate
differentials are also observed within each broad cause of death group. The results clearly
suggest the major epidemiological transition in India with increasing burden of deaths due to
non-communicable diseases in several states of India.
Key Words: Cause Specific Death Rate, Disease, Indirect Estimation
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CPS14: STATISTICAL APPLICATIONS IV
On Cluster Surveillance of Malnutrition Prevalence and Hunger Gaps in Kazaure Local
Government Area of Jigawa State, North-Eastern Nigeria
Mr. Anthony Ekpo *
University of Agriculture, Makurdi, Benue State, Nigeria
[email protected]
Mr. Enobong F. Udoumoh
University of Agriculture, Makurdi, Benue State, Nigeria
In our world today over one billion people are hungry and hence unable to meet their daily
calorie requirements and the most vulnerable in this group are children under 5 years. The
nutritional damage in early life can lead to permanent impairment, including lower Intelligent
Quotient (IQ), poor school performance, morbidity, mortality and lower economic status in
adulthood. This paper presents the result of a statistical surveillance system carried out in
Kazaure Local Government Area in Jigawa State, North-eastern Nigeria, using a multi-stage
cluster sampling technique with repeated surveys which stratified the Local Government Area
into three equal parts based on the principle of Population Proportional to Size (PPS). The
prevalence of Severe Acute Malnutrition (SAM) and Global Acute Malnutrition (GAM) were
estimated with the pattern of hunger gaps within the clusters at different survey times captured
from March 2010 to February 2012. The system was put in place as a pilot for future replication
and up-scaling to other states in Nigeria with malnutrition and food security challenges, if it
impacted on the wellbeing and livelihoods of the people. The study was designed such that data
were collected and analyzed with the application of the Emergency Nutrition Assessment (ENA)
which gave the point and interval estimates of malnutrition prevalence on a 95% confidence
interval with an assurance that the point and interval estimates were close enough to precision
and hence could be used as a basis for generalization on the entire Kazaure population.
Passive surveillance and constant monitoring at selected health centers in Kazaure Local
Government Area with systematic tracing of defaulters also helped to generate more data sets.
Through this system, a data driven and informed decisions on the prevalence of malnutrition
and the pattern of hunger gaps in Kazaure, targeting children between the ages of 6-59 months
or those children whose heights were between 65cm-110cm from 30 randomly selected clusters
were made.
Key Words: Mortality, Severe Acute Malnutrition, Global Acute Malnutrition, Mid-Upper Arm
Circumference, Anthropometrics and Bilateral Edema.
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ISI-RSC 2014 ABSTRACT BOOK
Multidimensional Poverty in Indonesia: A Spatial Analysis Using Geographically
Weighted Regression (GWR)
Wahyuni Andriana Sofa
BPS Statistics Indonesia, Jakarta, Indonesia
[email protected]
In a regional autonomy era, development strategies based on indigenous knowledge are always
needed because some cases may differ from one region to another, including poverty. The
limitation of the monetary approach in poverty analysis, is that it captures only a small number
of poverty issues. This gives rise to the need to develop a multidimensional poverty concept.
Based on the development result by Alkire and Foster (2007), this study aims to observe
multidimensional poverty in a spatial approach. If all data needed for this analysis were
completely available, it is more appropriate to use regression rather than Principal Component
(PCA) in poverty analysis. However, there are a number of assumptions underlying the basic
regression model that cannot be satisfied when spatial data are used in the model. The
characteristics of spatial data have many implications for the parameter estimates in the basic
model. /These lead to parameter estimates which can be both biased and inefficient. Therefore,
this study uses Exploratory Spatial Data Analysis (ESDA) to calculate spatial effect and
Geographically Weighted Regression (GWR) to develop models for 463 districts in Indonesia.
Specifically, it aims to determine the most dominant dimension of poverty; either health,
education or living standards and identify the pattern of spatial dependence on income poverty
rates. The result suggests that indicators of living standards have the strongest effect on poverty
rate among three dimensions. The spatial effect on poverty shown by interregional correlation is
proven here; poverty rate of a certain region significantly contributes to the poverty rate of its
neighbors. In this case, GWR is proven to significantly reduce this spatial effect based on its
variability and goodness of fit test. The spread of parameter estimate across region/districts
hopefully can be used as recommendation for government and stakeholders in terms of
deciding on of the types of social economic programs which should be prioritized.
Key Words: Multidimensional Poverty, Health, Education, Living Standard, Spatial Approach
The Spatial Extent of Land Use Externalities in the Fringe of Jakarta: Spatial
Econometrics Analysis
Rahma Fitriani*
Statistics Study Program, Department of Mathematics, University of Brawijaya, Jl. MT Haryono
169, Malang, Indonesia
[email protected]
Eni Sumarminingsih
Statistics Study Program, Department of Mathematics, University of Brawijaya, Jl. MT Haryono
169, Malang, Indonesia
[email protected]
It is well known that spatial externalities have important roles in determining land value, leading
to the application of spatial econometric models in the study of land use change. Recent studies
indicate that the interaction of two competing land use externalities creates sprawl –
leapfrogged urban spatial pattern in the fringe of Jakarta Metropolitan. This inefficient
development activity puts pressure on conservation areas and the productive agricultural sites
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ISI-RSC 2014 ABSTRACT BOOK
in the southern fringe. This motivated the study to analyse the extent of those externalities and
their role in the recent development activities in the area based on spatial econometric models.
For this study, two models were considered namely Spatial of Lag X (SLX) and Spatial Durbin
Model (SDM). Two variables (density and area proportion of agricultural activity) at district level
are used to capture the competing externalities (social and green externalities) of land use. The
proportion of developed area per district serves as a proxy for the development land value.
Bayesian MCMC is used to overcome the heterocesdasticity. The marginal posterior probability
shows that SDM is a better option. The model confirms the significant role of spatial externalities
on development activities, but only the social externalities that can extend globally.
Key Words: Externalities, Land Value, Sprawl, Spatial Model
Potential Environmental Impacts of Lands Drowning Along the Northern Nile Delta
Mostafa Mohamed Salah
Central Agency for Public Mobilization and Statistics (CAPMAS), Egypt
[email protected] or [email protected]
The current study aims to identify the expected impact of climate change and elevation of
sea surface rate on the productivity of agricultural lands in northern Nile Delta and the
impact on residents and coastal monumental sites, since the levels of Mediterranean
Sea waters may result in the loss of fertility in the lands in the northern Nile delta. The
increase in the environmental risks may threaten the Nile Delta in the coming raey which
in turn affects human and agricultural expansion. The researcher uses the anthological
method on a surveyed sample consisting of (248) experts such as university professors
from various research domains, with eight groups from various scientific sectors in
Egypt. The researcher uses specifically designed questionnaire as a study tool, and
interviews to collect data. The current study provides a future perspective. Hence, it combines
quantitative research tools (questionnaire form) and qualitative research tools (tellers, people in
charge of various scientific sectors who occupy administrative positions at the sector of climate
change, rise of the Mediterranean Sea surface, and its impact on the Nile delta lands.
Key Words: Environmental Risks, Spatial Statistics, Climate Change.
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