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On the Gaussian measure of the intersection of symmetric, convex sets

Gideon Schechtman, +1 more
- 18 Jul 1996 - 
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TLDR
The Gaussian Correlation Conjecture states that for any two symmetric, convex sets in n-dimensional space and for any centered, Gaussian measure on that space, the measure of the intersection is greater than or equal to the product of the measures as mentioned in this paper.
Abstract
The Gaussian Correlation Conjecture states that for any two symmetric, convex sets in n-dimensional space and for any centered, Gaussian measure on that space, the measure of the intersection is greater than or equal to the product of the measures In this paper we obtain several results which substantiate this conjecture For example, in the standard Gaussian case, we show there is a positive constant, c, such that the conjecture is true if the two sets are in the Euclidean ball of radius c √ n Further we show that if for every n the conjecture is true when the sets are in the Euclidean ball of radius √ n, then it is true in general Our most concrete result is that the conjecture is true if the two sets are

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Citations
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Geometry of Isotropic Convex Bodies

TL;DR: In this article, the hyperplane conjecture and Bourgain's upper bound are used to approximate the covariance matrix of a convex body and the thin shell conjecture. But they do not cover the problem of estimating the density of a body with maximal isotropic constant Logarithmic Laplace transform.
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Some Applications of Mass Transport to Gaussian-Type Inequalities

TL;DR: This map is used in the setting of Gaussian-like measures to derive an inequality linking entropy with mass displacement by a straightforward argument and logarithmic Sobolev and transport inequalities are recovered.
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A simple proof of the Gaussian correlation conjecture extended to multivariate gamma distributions

Thomas Royen
- 05 Aug 2014 - 
TL;DR: An extension of the Gaussian correlation conjecture (GCC) for multivariate gamma distributions (in the sense of Krishnamoorthy and Parthasarathy) is proved in this article.
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Chapter 4 - Convex Geometry and Functional Analysis

TL;DR: The Loomis-Whitney inequality as discussed by the authors is a generalization of the isoperimetric inequality, which was used in Gagliardo's proof of the Sobolev embedding theorem.
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Interpolation, correlation identities, and inequalities for infinitely divisible variables

TL;DR: In this paper, an interpolation formula for the expectation of functions of infinitely divisible (i.d.) variables is presented and applied to study the association problem for i.d. vectors and to present new covariance expansions and correlation inequalities.
References
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Rectangular Confidence Regions for the Means of Multivariate Normal Distributions

TL;DR: For rectangular confidence regions for the mean values of multivariate normal distributions, this paper proved that a confidence region constructed for independent coordinates is, at the same time, a conservative confidence region for any case of dependent coordinates.
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On extensions of the Brunn-Minkowski and Prékopa-Leindler theorems, including inequalities for log concave functions, and with an application to the diffusion equation

TL;DR: In this article, the authors extend the Prekopa-leindler theorem to other types of convex combinations of two positive functions and strengthen it by introducing the notion of essential addition.
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Classes of orderings of measures and related correlation inequalities. I. Multivariate totally positive distributions

TL;DR: In this article, a function f(x) defined on X = X 1 × X 2 × × × X n where each X i is totally ordered satisfying f (x ∨ y) f(xi ∧ y) ≥ f(y) f (y), where the lattice operations ∨ and ∧ refer to the usual ordering on X, is said to be multivariate totally positive of order 2 (MTP2).
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