Concentration of information content for convex measures

TL;DR

This paper derives sharp exponential deviation bounds for the information content and varentropy of convex measures, showing they are highly concentrated in high dimensions despite heavy tails, extending results known for log-concave measures.

Contribution

It generalizes concentration and deviation results from log-concave to convex measures, introducing new inequalities and extending classical inequalities like Berwald's.

Findings

Convex measures exhibit sharp concentration in high dimensions.

Established new bounds on varentropy for convex measures.

Extended classical inequalities to broader classes of measures.

Abstract

We establish sharp exponential deviation estimates of the information content as well as a sharp bound on the varentropy for the class of convex measures on Euclidean spaces. This generalizes a similar development for log-concave measures in the recent work of Fradelizi, Madiman and Wang (2016). In particular, our results imply that convex measures in high dimensions are concentrated in an annulus between two convex sets (as in the log-concave case) despite their possibly having much heavier tails. Various tools and consequences are developed, including a sharp comparison result for R\'enyi entropies, inequalities of Kahane-Khinchine type for convex measures that extend those of Koldobsky, Pajor and Yaskin (2008) for log-concave measures, and an extension of Berwald's inequality (1947).