Gaussian mixtures: entropy and geometric inequalities

Abstract

A symmetric random variable is called a Gaussian mixture if it has the same distribution as the product of two independent random variables, one being positive and the other a standard Gaussian random variable. Examples of Gaussian mixtures include random variables with densities proportional to $e^{-|t{|}^p}$ and symmetric $p$-stable random variables, where $p\in (0,2]$. We obtain various sharp moment and entropy comparison estimates for weighted sums of independent Gaussian mixtures and investigate extensions of the B-inequality and the Gaussian correlation inequality in the context of Gaussian mixtures. We also obtain a correlation inequality for symmetric geodesically convex sets in the unit sphere equipped with the normalized surface area measure. We then apply these results to derive sharp constants in Khintchine inequalities for vectors uniformly distributed on the unit balls with respect to $p$-norms and provide short proofs to new and old comparison estimates for geometric parameters of sections and projections of such balls.

Updated references and fixed a small error in Corollary 25

References in corpus (3)

• A probabilistic approach to the geometry of the \ell_p^n-ball
• On some inequalities for Gaussian measures
• On an Inequality of Karlin and Rinott Concerning Weighted Sums of i.i.d. Random Variables

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