Spherical thin-shell concentration for convex measures Matthieu Fradelizi, Olivier Gu´edon and Alain Pajor Abstract We prove that for s < 0, s-concave measures on Rn satisfy a spherical thin shell concentration similar to the log-concave one. It leads to a Berry-Esseen type estimate for most of their one dimensional marginal distributions. We also establish sharp reverse H¨older inequalities for s-concave measures. 1 Introduction Let s ∈ [−∞, 1]. A measure µ is called s-concave on Rn whenever µ ((1 − λ)A + λB) ≥ ((1 − λ)µ(A)s + λµ(B)s )1/s for every compact sets A, B ⊂ Rn such that µ(A)µ(B) > 0. When s = 0, this inequality should be read as µ ((1 − λ)A + λB) ≥ µ(A)1−λ µ(B)λ and it defines µ as a log-concave measure. When s = −∞, the measure is said to be convex and the inequality is replaced by µ ((1 − λ)A + λB) ≥ min (µ(A), µ(B)) . The purpose of this paper is to prove a spherical thin shell concentration for s-concave measures in the case s ≤ 0, which we consider from now on. By measure, we always mean probability measure. Notice that the class of s-concave measures on Rn is decreasing in s. In particular a log-concave 1 measure is s-concave for any s < 0 and any s-concave measure with s ≤ 0 is (−∞)-concave, thus a convex measure. The class of s-concave measures was introduced and studied in [9, 10], where the following complete characterization was established. An s-concave measure is supported on some convex subset of an affine subspace where it has a density. If the support of s-concave measure µ generates the whole space, then µ is absolutely continuous with respect to the Lebesgue measure and has a density h which is log-concave when s = 0 and when s < 0, is of the form 1 h = f −α with α = n − ≥ n, s n where f : R → (0, ∞] is a convex function. A random vector with an s-concave distribution is called s-concave. The linear image of a s-concave measure is also s-concave. It is known that any semi-norm of an n-dimensional vector with a s-concave distribution has moments up to the order p < −1/s (see [9] and [1]). Since we are interested in comparison of moments with the moment of order 2, we will always assume that −1/2 < s ≤ 0, which corresponds to α > n + 2. We say that a random vector X is isotropic if EX = 0 and for every θ ∈ S n−1 , EhX, θi2 = 1. For every −1/2 < s ≤ 0, we can always find an affine transformation A such that AX is isotropic. Our main result is the following Theorem 1. Let r > 2. Let X be a (−1/r)-concave isotropic random vector on Rn . Then for any p ∈ R such that 0 < |p| ≤ c min(r, n1/3 ), we have 3/5 C |p − 2| (E|X|p2 )1/p C |p − 2| − 1 ≤ + αp (X) := (E|X|22 )1/2 r n1/3 where C and c are universal constants. In the general case, let A be the affine transformation such that AX is 1/3 isotropic. Then for any p ∈ R such that 0 < |p| ≤ c min r, kAk2/3nkA−1 k2/3 , we have 3/5 (E|X|p2 )1/p C |p − 2| C |p − 2|(kAkkA−1 k)2/3 αp (X) := − 1 ≤ + (E|X|22 )1/2 r n1/3 where C and c are universal constants. 2 √ Remark 2. For r > n + n, we have a better inequality which recovers the log-concave estimate from [16]. To present the connections between moment inequalities, concentration in a spherical thin shell property and the Berry-Esseen theorem for one dimensional marginals, let us introduce some notations. Let X ∈ Rn be an isotropic random vector. Thus E|X|2 = n. Define ε(X) to be the smallest number ε > 0 such that |X| (1) P √ − 1 ≥ ε ≤ ε. n If ε(X) = o(1) with respect to the dimension n, we say that X is concentrated in a spherical thin shell. It was shown in [2] (see also [12, 13]) that if an isotropic random vector X uniformly distributed on a convex body in Rn is such that ε(X) = o(1), then almost all marginal distributions of X satisfy a Berry-Esseen theorem. More generally, let X ∈ Rn be an isotropic random vector, it was proved in [6] that 4 n−1 σn−1 θ ∈ S : sup |P(hX, θi ≤ t) − Φ(t)| ≥ 4ε(X) + δ ≤ 4n3/8 e−cnδ t∈R where σn−1 denotes the rotation invariant probability measure on the unit sphere S n−1 , Φ is the standard normal distribution function and c > 0 is a universal constant. Therefore if ε(X) is small then almost all the one dimension marginal distributions of X are approximately Gaussian. The fact that indeed for all log-concave random vector ε(X) = o(1) was proved later in [18, 15] and the best estimate at this date [16] is that ε(X) = O(n−1/6 log n). Now let p > 2 and assume that |X|2 has finite p moment. For an isotropic random vector X, αp (X) = (E|X|p2 )1/p (E|X|p2 )1/p √ − 1 = − 1. (E|X|22 )1/2 n In the particular case p = 4, it is easy to deduce from Markov inequality that ε(X) ≤ (24 α4 (X))1/3 . Thus if α4 (X) is small, then so is ε(X) and from this discussion, it follows that almost all one dimensional marginal 3 distributions of X are approximately Gaussian. Hence Theorem 1 ensures that if r → +∞ with the dimension n then any isotropic (−1/r)-concave vector satisfies a spherical thin shell concentration and therefore almost all its marginals verify a Berry-Esseen theorem. As a matter of fact, this condition on r is necessary. If r is fixed and does not depend on the dimension n, we give below an example of an isotropic (−1/r)-concave random vector X ∈ Rn which does not satisfy a spherical thin shell property. This shows the sharpness of Theorem 1 for small values of r. Fact: Let r > 2. For every n ≥ 1, there exists an isotropic (−1/r)-concave random vector Xn ∈ Rn such that ε(Xn ) ≥ c(r) > 0 where c(r) > 0 depends only on r. Moreover, when r tends to ∞, one has lim αp (Xn ) ∼ n→∞ p−2 . r−p (2) Proof. Let r > 2 and 2 < p < r and let Xn ∈ Rn be an isotropic random vector with density c1 fn,r (x) = (1 + c2 |x|2 )r+n where c1 and c2 are normalization factors. Such a random vector is (−1/r)concave. An immediate computation gives that (E|Xn |p2 )1/p = (E|Xn |22 )1/2 B(n + p, r − p) B(n, r) 1/p B(n + 2, r − 2) B(n, r) −1/2 . For fixed r and 2 < p < r, we have (E|Xn |p2 )1/p lim = 1/2 n→+∞ (E|Xn |2 2) Γ(r − p) Γ(r) 1/p Γ(r − 2) Γ(r) Therefore, when r → +∞ lim αp (Xn ) ∼ n→+∞ 4 p−2 . r−p −1/2 6= 1. (3) From Paley-Zygmund inequality we get that for any 2 < p < s < r P |Xn | √ ≥1+ε n (αp + 1)p − (1 + ε)p ≥ (αs + 1)sp 1/(s−p) . For fixed r, 2 < p < r and n large enough, choosing for instance p = (2+r)/2 and s = (p + r)/2, we get from (3) that |Xn | P √ ≥ 1 + ε ≥ c(r) > 0 n for every 0 < ε ≤ ε(r), where ε(r) and c(r) > 0 depend only on r. Therefore ε(Xn ) ≥ min(ε(r), c(r)) > 0. To build the proof of Theorem 1, we need to extend to the case of sconcave measures several tools coming from the study of log-concave measures. This is the purpose of Section 2. Some of them were already achieved by Bobkov [7], like the analogous of the Ball’s bodies [5] in the s-concave setting. Some others were also noticed previously (see e.g. [7], [1]) but not with the most accurate point of view. These new ingredients are analogous to the results of [11] in the log-concave setting and are at the heart of our proof. As in the approach of [14] or [16], an important ingredient is the LogSobolev inequality on SO(n). It allows to get reverse H¨older inequalities, see [21, 14]: for every f : SO(n) → R, if L is the Log-Lipschitz constant of f then for every q > r > 0, 2 cL q 1/q (q − r) (EU |f |r )1/r . (4) (EU |f | ) ≤ exp n According to the characterization of −1/r-concave measures in [10], let X be a (−1/r)-concave random vector in Rn with full dimensional support and distributed according to a measure with a (−1/α)-concave density function w : Rn → R+ with α = r + n. For any linear subspace E, denote by PE the orthogonal projection onto E and for any x ∈ E denote by Z πE w(x) = w(y)dy x+E ⊥ 5 the marginal of w on E. Given an integer k between 1 and n, a real number p ∈ (−k, r), a linear subspace E0 ∈ Gn,k and θ0 ∈ S(E0 ), define the function hk,p : SO(n) → R+ : Z ∞ k−1 tp+k−1 πU (E0 ) w(tU (θ0 ))dt. (5) hk,p (U ) := Vol(S ) 0 Following the approach of [19, 14], we observe that for any p ∈ (−k, r) E|X|p = Γ((p + n)/2)Γ(k/2) EU hk,p (U ) Γ(n/2)Γ((p + k)/2) (6) where U is uniformly distributed on SO(n). In view of (6) and the definition of hk,p , we notice that it is of importance to work with family of measures which are stable after taking the marginals. For any subspace E of dimension k, since X is (−1/r)-concave with density w, we know that PE X is (−1/r)concave and πE w is (−1/αk )-concave with αk = r + k. 2 Preliminary results for s-concave measures Theorem 3. Let α > 0 and f : [0, ∞) → [0, ∞) be (−1/α)-concave and integrable. Define Hf : [0, α) → R+ by Z +∞ 1 tp−1 f (t)dt, 0 < p < α Hf (p) = B(p, α − p) 0 f (0) p=0 R1 R +∞ where B(p, α − p) = 0 up−1 (1 − u)α−p−1 du = 0 tp−1 (t + 1)−α dt. Then Hf is log-concave on [0, α). Since the proof is identical to the well known 1/n-concave case [11], we postpone it to the Appendix. We present several consequences of this result like some reverse H¨older inequalities with sharp constants in the spirit of Borell’s [11] and Berwald’s [3] inequalities. Corollary 4. Let r > 0 and µ be a (−1/r)-concave measure on Rn . Let φ : Rn → R+ be concave on its support. Then Z 1 p 7→ φ(x)p dµ(x) pB(p, r − p) 6 is log-concave on [0, r). Moreover, for any 0 < p ≤ q < r, 1/q 1/p Z (qB(q, r − q))1/q p φ(x) dµ(x) ≤ φ(x) dµ(x) (pB(p, r − p))1/p Rn Rn Z q and for every θ ∈ S n−1 , Z 1/q Z 1/p (qB(q, r − q))1/q q p hx, θi+ dµ(x) ≤ hx, θi+ dµ(x) (pB(p, r − p))1/p Rn Rn where hx, θi+ = hx, θi 0 (7) if hx, θi ≥ 0 otherwise. Proof. By concavity of φ, for every u, v ≥ 0, and any λ ∈ [0, 1] (1 − λ){φ ≥ u} + λ{φ ≥ v} ⊂ {φ ≥ (1 − λ)u + λv}. By −1/r-concavity of µ, the function f (t) = µ({φ > t}) is −1/r-concave. Observe by Fubini that for any p > 0, Z Z +∞ p φ(x) dµ(x) = ptp−1 f (t)dt. Rn 0 The result follows from Theorem 3. Since µ is a probability we have f (0) = 1 = Hf (0) and the moreover part follows from the fact that p 7→ Hf (p)1/p is a non increasing function. For the last inequality, we notice that for every θ ∈ S n−1 , the function φ(x) = hx, θi+ is affine on its support. The second corollary concerns the function hk,p . Corollary 5. Let r > 0. For any −1/(r +n)-concave function w : Rn → R+ , and any subspace F of dimension k ≤ n, hk,p (U ) , p > −k + 1, p 7→ B(p + k, r − p) Vol(S k−1 )πU (E0 ) w(0), p = −k + 1 is log-concave on [−k + 1, r). Proof. Since w is −1/(r + n)-concave, we note that t 7→ πU (E0 ) w(tU (θ0 )) is −1/(r + k)-concave. Theorem 3 proves the result. 7 We finish with some geometric properties of a family of bodies introduced by K. Ball in [5] in the log-concave case. Corollary 6. Let α > 0. Let w : Rn → R+ be a −1/α-concave function such that w(0) > 0. For 0 < a < α let Z +∞ a−1 n t w(tx)dt ≥ w(0) . Ka (w) = x ∈ R ; a 0 Then for any 0 < a ≤ b < α 1−1 w(0) a b (bB(b, α − b))1/b Ka (w) ⊂ Kb (w) ⊂ Ka (w). kwk∞ (aB(a, α − a))1/a Proof. Notice that the sets Ka are star-shaped with respect to the origin. For any x ∈ Rn , let f be the −1/α-concave function defined on R+ by f (t) = w(tx)/w(0). By Theorem 3, the function Hf : [0, α) → R+ is logconcave on [0, α) with Hf (0) = 1. For 0 < a ≤ b < α, we have thus Hf (b)1/b ≤ Hf (a)1/a . The right hand side inclusion follows. The left hand side is a simple consequence of H¨older’s inequality in the even case. The non-even case may be easily treated as Lemma 2.1 in [22]. 3 Thin shell for convex measures The purpose of this section is to prove Theorem 1. We follow the strategy from the log-concave case initiated in [18, 15, 19] and further developed in [14, 16]. To any random vector X in Rn and any p ≥ 1, we associate its Zp+ -body defined by its support function 1/p hZp+ (X) (θ) = E hX, θip+ . When the distribution of X has a density g, we write Zp+ (g) = Zp+ (X). Extending a theorem of Ball [5] on log-concave functions, Bobkov proved in [7] that if w is a −1/(r + n)-concave on Rn such that w(0) > 0, then Ka (w) is convex for any 0 < a ≤ r + n − 1. In the case of log-concave measures [23, 24, 16, 17], several relations between the Zp+ bodies and the convex sets Ka are known. We need their analogue in the setting of s-concave measures for negative s. We start with two technical lemmas. We postpone their proofs to the Appendix. 8 Lemma 7. Let x, y ≥ 1, then x x c ≤ (xB(x, y))1/x ≤ C , x+y x+y (8) where c, C are positive universal constants. Moreover for k, r > 1, we have B(k + p, r − p) 1 1 d 1 log ≤ + (9) 0≤ dp p B(k, r) r−1 k−1 for |p| ≤ (min(r, k) − 1)/2. Lemma 8. Let r, m and p be such that m is an integer, r ≥ m + 1 and − m2 ≤ p ≤ r − 1, Let F be a subspace of dimension m and let g be a −1/(r + m)-concave density of a probability measure on F with barycenter at the origin. Then we have + d(Km+p (g), B2 (F )) ≤ c d(Zmax(m,p) (g), B2 (F )) where c is a universal constant. An important ingredient in the proof of a thin-shell concentration inequality is an estimate from above of the log-Lipschitz constant of U 7→ hk,p (U ). Proposition 9. Let n ≥ 1, r > 10 and w be a −1/(r + n)-concave density of a probability measure on Rn . Let k be an integer such that 2k + 1 ≤ n and 2k + 2 ≤ r. Let p such that − k2 ≤ p ≤ r − 1. Denote by Lk,p the log-Lipschitz constant of U 7→ hk,p (U ). Then + Lk,p ≤ C max(k, p)d(Zmax(k,p) (w), B2n ) where C is a universal constant. Proof. Since Ka (w) are appropriate convex bodies, the proof of Theorem 2.1 in [16] works identically and lead to the upper bound : max{(m + p) d(Km+p (πF (w)), B2 (F ))} F over all subspaces F of dimension m = k, k + 1, 2k + 1. From Lemma 8, we have + d(Km+p (πF (w)), B2 (F )) ≤ c d(Zmax(p,m) (πF (w)), B2 (F )). By integration in polar coordinates, Zp+ (πF (w)) = PF (Zp+ (w)) and the distance to the Euclidean ball cannot increase after projections. Hence + d(Km+p (πF (w)), B2 (F )) ≤ cd(Zmax(p,m) (w), B2n ). 9 We define the q-condition number of a random vector X to be 1/q sup|θ|2 =1 E hX, θiq+ ρq (X) = 1/q . inf |θ|2 =1 E hX, θiq+ Obviously, if w is the density of X, ρq (X) = d(Zq+ (w), B2n ). Proposition 10. With the same assumptions as in Proposition 9, if the random vector X with density w is isotropic then Lk,p ≤ C max(k, p)2 . More generally if A is such that AX is isotropic then Lk,p ≤ C max(k, p)2 kAkkA−1 k. (10) Proof. Let q = max(k, p) then one has 1 ≤ q ≤ r − 1. If X is isotropic then from Corollary 4 and equation (8) we get that for q ≥ 2 ρq (X) ≤ (qB(q, r − q))1/q 1/2 (2B(2, r − 2)) ≤ Cq/r ≤ c0 q, c/r for some universal constant c0 > 0. The same inequality holds for q ∈ [1, 2]. The conclusion follows from Proposition 9. In the general case, notice that Zq+ (AX) = AZq+ (X) and d(AB2n , B2n ) = kAkkA−1 k thus ρq (X) ≤ ρq (AX)kAkkA−1 k. Proof of Theorem 1. We start by presenting a complete argument following [14]. This will give a complete proof of Theorem 1 with a slightly weaker result. In the second part, we just indicate the needed modifications of the argument of [16] to get the complete conclusion. From now on, we assume without loss of generality that r > 10 (otherwise the inequality of Theorem 1 is obvious, see [1]) and |p| ≤ (r − 2)/4. Let k be an integer such that −p < k ≤ n. We will optimize the choice of k at the end of the proof. Recall that by (6), E|X|p = Γ((p + n)/2)Γ(k/2) EU hk,p (U ) Γ(n/2)Γ((p + k)/2) 10 where U is uniformly distributed on SO(n). Then for all 0 < p < r and n ≥ k > p we have B((n + p)/2, (n − p)/2) EU hk,p (U )EU hk,−p (U ) B((k + p)/2, (k − p)/2) ≤ EU hk,p (U )EU hk,−p (U ) (11) E|X|p E|X|−p = since B((n+p)/2, (n−p)/2) ≤ B((k+p)/2, (k−p)/2). Applying Log-Sobolev inequality (4) to hk,p and hk,−p we get EU h2k,p ≤ e c L2 k,p n (EU hk,p )2 and EU h2k,−p ≤ e c L2 k,−p n (EU hk,−p )2 . (12) Since Varf = Ef 2 − (Ef )2 we deduce that Varhk,p ≤ (e c L2 k,p n − 1) (EU hk,p )2 , Varhk,−p ≤ (e c L2 k,−p n − 1) (EU hk,−p )2 . (13) By Corollary 5, we know that p 7→ hk,p (U )/B(k + p, r − p) is log-concave on [−k + 1, r) hence B(k + p, r − p) B(k − p, r − p) hk,p (U ) hk,−p (U ) ≤ h2k,0 (U ). B(k, r) B(k, r) Taking the expectation with respect to SO(n), we get that B(k + p, r − p) B(k − p, r − p) EU h2k,0 (U ). EU hk,p (U )hk,−p (U ) ≤ B(k, r) B(k, r Since EU hk,0 (U ) = 1 we deduce from (12) that EU h2k,0 (U ) ≤ e c L2 k,0 n . By (9), we know that for p ≤ (k − 1)/2, B(k + p, r − p) B(k − p, r − p) B(k, r) B(k, r) hence EU hk,p (U )hk,−p (U ) ≤ e 11 1/p c L2 k,0 n ≤ e4p(1/k+1/r) +4p2 ( k1 + r1 ) . (14) Moreover EU hk,p (U ) hk,−p (U ) = EU hk,p (U ) EU hk,−p (U ) + Cov(hk,p , hk,−p ) p ≥ EU hk,p (U ) EU hk,−p (U ) − Varhk,p Varhk,−p s c L2 ! c L2 k,p k,−p e n −1 e n −1 ≥ EU hk,p (U ) EU hk,−p (U ) 1 − where the last inequality follows from (13). For p ≤ (k−1)/2, we can evaluate Lk,p , Lk,−p and Lk,0 from Proposition 10 since the assumptions are full filled. We get that if X is isotropic then max(Lk,p , Lk,−p , Lk,0 ) ≤ Ck 2 . Combining the previous estimate with (14) we get that if k ≤ cn1/4 and p ≤ (k − 1)/2 then 2 4 e4p (1/k+1/r)+ck /n r c k4 EU hk,p (U )hk,−p (U ) c k4 1− e n −1 e n −1 k4 1 1 2 + ≤ 1 + Cp +C . k r n EU hk,p (U ) EU hk,−p (U ) ≤ We conclude from (11) that p −p 1/p (E|X| E|X| ) ≤ 1 + Cp 1 1 + k r k4 +C n . It remains to optimize the choice of k. If 0 < p < n−1/2 then we choose k = 5 and get C C p −p 1/p . (15) (E|X| E|X| ) ≤ 1 + + √ r n If p ≥ n−1/2 we can choose k such that k 5 = p2 n with the restriction 2p + 1 ≤ k ≤ cn1/4 . This proves that 3/5 ! Cp Cp . ∀n−1/2 ≤ p ≤ c n1/8 , (E|X|p E|X|−p )1/p ≤ 1 + + r n1/3 Therefore for any p such that |p| ≤ min(c n1/8 , (r − 2)/4), 3/5 ! (E|X|p )1/p C(1 + |p|) C(1 + |p|) , + (E|X|2 )1/2 − 1 ≤ r n1/3 12 which is already enough to get a spherical thin shell concentration. In the second part, we follow the argument developed in [16] to get a better conclusion. We continue to assume that |p| ≤ (r − 2)/4. Most of the computation of section 3.2 in [16] is identical and it required the use of the reverse H¨older inequalities (12). We estimate 1 Γ((p + n)/2)Γ(k/2) d d d 1 p p1 p log((E|X| ) ) = log((EU hk,p (U )) )+ log . dp dp dp p Γ(n/2)Γ((p + k)/2) By Corollary 5 p 7→ hk,p (U ) , B(p + k, r − p) is log-concave on [−k + 1, r). This can be written as hk,0 (U ) d 1 hk,p (U ) − log log ≤ 0. dp p B(p + k, r − p) B(k, r) We know from Lemma 7 that for all p ∈ [− k−1 , r−1 ] 2 2 B(k + p, r − p) d 1 1 1 log + ≤C . dp p B(k, r) k r Following Section 3.2 in [16], we arrive at 1 c d C C log((E|X|p ) p ) ≤ 2 (2L2k,p + 3L2k,0 ) + + . dp pn k r Assume that X is isotropic. By Proposition 10, we know that for any k ≥ 2|p| and k ≤ (r − 2)/2, Lk,p , Lk,−p , Lk,0 are smaller than Ck 2 . We get that 4 d k 1 1 p p1 log((E|X| ) ) ≤ C + + . dp p2 n k r We have to minimize this expression for k ∈ [2|p|, (r − 2)/2]. For |p| ≤ cn1/3 , we set k = min((r − 2)/2, c(p2 n)1/5 ) so that k ∈ [2|p|, (r − 2)/2] and we get d c 1 p p1 log((E|X| ) ) ≤ C + . dp (p2 n)1/5 r Let p0 = n−1/2 , we get after integration that ∀p ∈ [p0 , c min(r, n1/3 )], 3/5 (E|X|p )1/p C |p − 2| C |p − 2| + (E|X|2 )1/2 − 1 ≤ r n1/3 13 and ∀p ∈ [−c min(r, n1/3 ), −p0 ], 3/5 C |p − p0 | (E|X|p )1/p C |p − p0 | + . (E|X|−p0 )−1/p0 − 1 ≤ r n1/3 For p ∈ [−p0 , p0 ], we use (15) and this concludes the proof of Theorem 1. If X is such that AX is isotropic, we know from Proposition 10 that for any k ≥ 2|p|, max(Lk,p , Lk,−p , Lk,0 ) ≤ Ck 2 kAkkA−1 k. The proof is identical to the previous one replacing n by n . kAk2 kA−1 k2 Remark 11. In [16], a preprocessing step was to add a Gaussian isotropic vector to get from the very beginning a better information on the Zp+ bodies associated to the measure. In [19], [14], this convolution argument played a role of regularization. It is natural to ask if such a process could be done in the situation of s-concave measure. Nothing is doable by adding a Gaussian vector because for s < 0, the new vector does not belong to any class of s-concave vectors. However, we can build a similar argument for r > n, taking the addition of X with a random vector Z uniformly distributed on the isotropic Euclidean ball, see also [8]. Since Z is 1/n-concave and X 1 √ will be a − r−n -concave random is −1/r-concave, the new vector Y = X+Z 2 isotropic vector. For any p ≥ 1, we have (see inequality (4.7) in [16]) αp (X) ≤ α2p (Y ) (2 + α2p (Y )) so that it remains to bound α2p (Y ). It is easy to see that Y is such that for 1/q √ every q ≥ 2 and every θ ∈ S n−1 , E hY, θiq+ ≥ c q. Adapting the proof of Proposition 10, we get Lk,p ≤ C max(k, p)3/2 . As in [16], this improvement leads to the √ estimate : if r − n > 2, then for any p such that 1 ≤ p ≤ c min(r − n, n) 1/2 C(2p − 2) C(2p − 2) √ + . α2p (Y ) ≤ r−n n √ For r > n + n, we recover the same spherical thin shell concentration as in the log-concave case. It would be interesting to understand in which precise sense the s-concave measures are close to the log-concave measures for s ∈ (−1/n, 1/n). An other question is to know what kind of preprocessing argument like in [20] would enable to recover the small ball estimates from [1]. 14 4 Appendix Proof of Theorem 3. Since f is −1/α-concave, there exists a convex function ϕ : [0, ∞) → (0, ∞) such that f = ϕ−α . Since f is integrable it follows that ϕ tends to +∞ at +∞. From the convexity of ϕ, one deduces that for some constant c > 0, ϕ(t) ≥ (1 + t)/c. Thus f (t) ≤ c(1 + t)−α , for every t ≥ 0. Therefore, tp−1 f is integrable for every p < α, which means that Hf (p) < +∞ for every 0 ≤ p < α. For any p ∈ (0, α) and any m, M > 0, we have −α Z 1 Z +∞ t p p−1 v p−1 (1 − v)α−p−1 dv (16) 1+ dt = mM mt M 0 0 = mM p B(p, α − p). Take 0 < a < b < c < α. Choose m and M such that mM a = Hf (a) and mM b = Hf (b) so that Hg (a) = Hf (a) and Hg (b) = Hf (b), where g : −α R+ → R+ is the function defined by g(t) = m 1 + Mt . Then we observe from (16) that Hg ((1 − λ)a + λc) = Hg (a)1−λ Hg (c)λ where λ is such that b = (1 − λ)a + λc. Our goal is to prove that Z +∞ tc−1 (g − f )(t)dt ≥ 0. (17) 0 We will then deduce that Hf (b) = Hg (b) = Hg (a)1−λ Hg (c)λ ≥ Hf (a)1−λ Hf (c)λ and this will prove the log-concavity of H on (0, α). Let h = (g − f ). Let Z +∞ Z +∞ a−1 sb−a−1 H1 (s)ds. s h(s)ds, and H2 (t) = H1 (t) = t t α Using that h(t) ≤ c/(1 + t) , on R+ , one can see that for every t ≥ 0 c c H1 (t) ≤ and H (t) ≤ . 2 (1 + t)α−a (1 + t)α−b R +∞ We have 0 ta−1 h(t)dt = 0 thus H1 (∞) = H1 (0) = 0. Obviously H2 (∞) = 0. We also observe Z +∞ Z +∞ Z +∞ b−1 b−a a−1 0= t h(t)dt = t t h(t)dt = − tb−a H10 (t)dt 0 0 0 Z +∞ = [tb−a H1 (t)]+∞ + (b − a) tb−a−1 H1 (t)dt = (b − a)H2 (0), 0 0 15 whence H2 (∞) = H2 (0) = 0. Since H20 (t) = −tb−a−1 H1 (t), the function H1 changes sign at least once (if not, then H20 ≥ 0 or H20 ≤ 0, but since H2 (0) = H2 (∞) = 0, we have H2 ≡ 0, but then H1 ≡ 0, h ≡ 0 and there is nothing to do). Since H1 changes sign at least once and H1 (0) = H1 (∞) = 0, therefore H10 changes of sign at least twice. Since H10 (t) = −ta−1 h(t), we have that h changes sign at least twice, but since g −α is affine and f −α is convex, h changes sign exactly twice and we have g −α (0) < f −α (0) so that h(0) > 0. We deduce that H2 ≥ 0. Therefore, Z +∞ Z +∞ Z +∞ c−1 c−a a−1 t h(t)dt = t t h(t)dt = − tc−a H10 (t)dt 0 0 Z0 +∞ = [−tc−a H1 (t)]+∞ + (c − a) tc−a−1 H1 (t)dt 0 0 Z +∞ = (c − a) tc−b tb−a−1 H1 (t)dt 0 Z +∞ c−b +∞ = [−t H2 (t)]0 + (c − a)(c − b) tc−b−1 H2 (t)dt ≥ 0. 0 This proves (17). Thus the log-concavity of H on (0, α). To get it on [0, α), it is enough to prove that H is continuous at 0. This is classical, but let us recall the argument for completeness. Since f is −1/α-concave, for all 0 ≤ s ≤ kf k∞ , its level sets {f ≥ s} are convex in [0, +∞) thus there exists 0 ≤ a(s) ≤ b(s) ≤ kf k∞ such that Z +∞ Z kf k∞ Z b(s) p−1 pt f (t)dt = ptp−1 dtds. 0 0 a(s) Since a(s) = inf{t ≥ 0; f (t) ≥ s} is non increasing on [0, kf k∞ ], one has a(s) = 0 for 0 ≤ s ≤ f (0), thus Z +∞ Z f (0) Z b(s) Z kf k∞ Z b(s) p−1 p−1 pt f (t)dt = pt dtds + ptp−1 dtds 0 0 Z = 0 f (0) f (0) b(s)p ds + 0 Z a(s) kf k∞ (b(s)p − a(s)p )ds. f (0) R +∞ We conclude that 0 ptp−1 f (t)dt tends to f (0) when p tends to 0. Applied to both f and (1 + t)−α this proves that Hf is continuous at 0. 16 Proof of Lemma 7. Equation (8) follows easily from the classical bounds for the Gamma function (see [4]), valid for x ≥ 1: √ √ 1 1 1 2πxx− 2 e−x ≤ Γ(x) ≤ 2πxx− 2 e−x+ 12 . For equation (9), we write that B(k + p, r − p) Γ(k + p)Γ(r − p) = . B(k, r) Γ(k)Γ(r) P Denote G(p) = log Γ(p), for p > 0. We know that G00 (p) = i≥0 1/(p + i)2 hence G00 is non increasing and 0 ≤ G00 (p) ≤ 1/(p − 1), for p > 1. Denote R1 Fk (p) = G(k+p)−G(k) , for k > 0 and p > −k. We have Fk (p) = 0 G0 (k+up)du. p Using that G00 is non increasing, we get that for k > 1 and p ≥ −(k − 1)/2, Z 1 Z 1 k+1 1 00 k + 1 1 00 00 0 G (k+up)udu ≤ G udu = G Fk (p) = ≤ . 2 2 2 k−1 0 0 Therefore, if p ≤ (min(r, k) − 1)/2 then B(k + p, r − p) d 1 d log (Fk (p) − Fr (−p)) = dp p B(k, r) dp = Fk0 (p) + Fr0 (−p) ≤ 1 1 + . k−1 r−1 Proof of Lemma 8. We present here a similar but simpler proof than in the Appendix of [16]. By integration in polar coordinates, it is well known [23] (see also [17]) that we have the following relation between the Zq+ -bodies associated with g and the Zq+ -bodies associated with one of the convex bodies Ka (g): for any 0 < q < r Zq+ (g) = g(0)1/q Zq+ (Km+q (g)). (18) Let us prove that for any convex body K ⊂ Rm containing 0 in its interior, for any q ∈ (0, +∞), and for every θ ∈ Rn hK (θ) ≥ hZq+ (K) (θ) Vol(K ∩ {hx, θi ≥ 0})1/q 17 ≥ (qB(q, m + 1))1/q hK (θ). (19) Indeed, observe that hqZ + (K) (θ) q Z = hx, θiq+ dx Z +∞ =q tq−1 f (t)dt, 0 K where f (t) = Vol({x ∈ K : hx, θi ≥ t}) is a 1/m-concave function on (0, +∞). The analogous of Theorem 3 in the 1/m-concave case [11] (see also [17] for a modern presentation) tells that the function Gf : [0, ∞) → R+ defined by R +∞ q−1 0 t f (t)dt q>0 Gf (q) = B(q, m + 1) f (0) q=0 1/q Gf (q) is log-concave on [0, +∞). Hence Gf (0) is nonincreasing. Since hZq+ (K) (θ) = (qB(q, m + 1))1/q Gf (q)1/q and Gf (0) = f (0) = Vol(K ∩ {hx, θi ≥ 0}), we get for any q ∈ (0, +∞), hZq+ (K) (θ) (qB(q, m + 1)Vol(K ∩ {hx, θi ≥ 0}))1/q is greater than the limit of the same quantity when q goes to +∞. Since 0 is in the interior of K, we have lim q→+∞ hZq+ (K) (θ) Vol(K ∩ {hx, θi ≥ 0})1/q = max hx, θi = hK (θ). x∈K A computation gives that (qB(q, m + 1))1/q ∼q→+∞ 1 and this proves (19). First assume that p ≥ m. Take q = max(p, m) = p and K = Km+p . By Lemma 7, for any p ≥ m ≥ 1, (pB(p, m + 1))1/p ≥ cp/(m + p + 1) ≥ c/3 and we deduce from (19) that c Km+p ⊂ 1 Zp+ (Km+p ) ⊂ Km+p 1/p Vol(Km+p ∩ {hx, θi ≥ 0}) where c is a universal constant. We conclude from (18) that d(Km+p , Zp+ (g)) ≤ c 18 for a universal constant c. This gives the conclusion. In the case when − m2 < p < m, we get from Corollary 6 g(0) kgk∞ 1 1 m+p − 2m Km+p ⊂ K2m ⊂ ((2m)B(2m, r − m))1/2m Km+p . ((m + p)B(m + p, r − p))1/m+p From Lemma 7, we have ((2m)B(2m, r − m))1/2m 2m ≤ 4c ≤c 1/m+p ((m + p)B(m + p, r − p)) m+p since p ≥ − m2 . For the other inclusion, we use the fact that g has barycentre at the origin and the sharp lower bound of the ratio g(0)/kgk∞ , proved in Lemma 20 of [1], r+m r−1 g(0) ≥ ≥ e−2m kgk∞ r+m−1 for m ≤ r − 1. Since p ≥ − m2 , we have (m − p)/(m + p) ≤ 3 hence g(0) kgk∞ 1 1 m+p − 2m ≥ e−3 . Therefore d(Km+p , K2m ) ≤ c where c is a universal constant. We conclude + (g), B2 ) and this finthat d(Km+p (g), B2 (F )) ≤ cd(K2m (g), B2 (F )) ≤ c0 d(Zm ishes the proof. Acknowledgement. The research of the three authors is partially supported by the ANR GeMeCoD, ANR 2011 BS01 007 01. References [1] R. Adamczak, O. Gu´edon, R. Latala, A.E. Litvak, K. Oleszkiewicz, A. Pajor and N. Tomczak-Jaegermann, Moment estimates for convex measures, Electr. J. Probab. 17 (2012), no 101, 1–19. [2] M. Anttila, K. Ball and I. Perissinaki, The central limit problem for convex bodies, Trans. Amer. Math. Soc. 355 (2003), no. 12, 4723–4735. 19 [3] L. Berwald, Verallgemeinerung eines Mittelwertsatzes von J. Favard f¨ ur positive konkave Funktionen, Acta Math., 79, (1947) 17–37. [4] E. Artin, The gamma function. Athena Series: Selected Topics in Mathematics, Holt, Rinehart and Winston, New York-Toronto-London 1964 vii+39 pp. [5] K. Ball, Logarithmically concave functions and sections of convex sets in Rn , Studia Math. 88 1 (1988), 69–84. [6] S. G. Bobkov, On concentration of distributions of random weighted sums, Annals of Prob. (2003). [7] S.G. Bobkov, Convex bodies and norms associated to convex measures, Probab. Theory Related Fields, 147 (2010), 303–332. [8] S.G. Bobkov and M. Madiman, Reverse Brunn-Minkowski and reverse entropy power inequalities for convex measures, J. Funct. Anal., 262 (2012), no. 7, 3309–3339. [9] C. Borell, Convex measures on locally convex spaces, Ark. Math. 12 (1974), 239–252. [10] C. Borell, Convex set functions in d-space, Period. Math. Hungar., 6 (1975), 111–136. [11] C. Borell, Complements of Lyapunov’s inequality, Math. Ann., 205 (1973), 323–331. [12] U. Brehm, J. Voigt, Asymptotics of cross sections for convex bodies. Beitr¨age Algebra Geom., 41 (2000), 437–454. [13] U. Brehm, P. Hinow, H. Vogt, H., J. Voigt, Moment inequalities and central limit properties of isotropic convex bodies. Math. Z., 240 (2002) 37–51. [14] B. Fleury, Concentration in a thin Euclidean shell for log-concave measures, Journal of Functional Analysis 259 (2010) 832–841. [15] B. Fleury, O. Gu´edon, and G. Paouris, A stability result for mean width of Lp -centroid bodies, Adv. Math., 214 (2007) 865–877. 20 [16] O. Gu´edon and E. Milman, Interpolating thin-shell and sharp largedeviation estimates for isotropic log-concave measures, Geom. Funct. Anal. 21 (2011), 1043–1068. [17] O. Gu´edon, P. Nayar and T. Tkocz, Concentration inequalities and geometry of convex bodies. Notes of a eerie of lectures given by O. Gu´edon in Warsaw in 2011. [18] B. Klartag, A central limit theorem for convex sets, Invent. Math., 168 (2007), 91–131. [19] B. Klartag, Power-law estimates for the central limit theorem for convex sets, J. Funct. Anal. 245 (2007), 284–310. [20] B. Klartag and E. Milman, Inner regularization of log-concave measures and small-ball estimates. Geometric aspects of functional analysis, 267– 278, Lecture Notes in Math., 2050, Springer, Heidelberg, 2012. [21] M. Ledoux, The concentration of measure phenomenon, Mathematical Surveys and Monographs, 89. American Mathematical Society, Providence, RI, 2001. [22] V.D. Milman and A. Pajor, Isotropic position and inertia ellipsoids and zonoids of the unit ball of a normed n-dimensional space, Lectures Notes in Mathematics 1376, Springer, Berlin, 1989, 107–131. [23] G. Paouris, Concentration of mass on convex bodies, Geom. Funct. Anal. 16 (2006), 1021–1049. [24] G. Paouris, Small ball probability estimates for log-concave measures, Trans. Amer. Math. Soc. (2012), 287–308. Matthieu Fradelizi, Olivier Gu´edon, Alain Pajor Universit´e Paris-Est Laboratoire d’Analyse et Math´ematiques Appliqu´ees (UMR 8050). UPEMLV, F-77454, Marne-la-Vall´ee, France e-mail: [email protected], [email protected], [email protected] 21
© Copyright 2024