A combinatorial approach to small ball inequalities for sums and differences

TL;DR

This paper develops sharp, distribution-free inequalities for small ball probabilities of sums and differences of i.i.d. random elements in various algebraic and geometric settings, linking probabilistic bounds with combinatorial extremal problems.

Contribution

It introduces a novel combinatorial framework to derive small ball inequalities applicable across diverse mathematical structures, extending previous Gaussian-focused results.

Findings

Established sharp inequalities for small ball probabilities in general settings

Connected probabilistic inequalities with classical packing problems like the kissing number

Provided applications to moment inequalities in probability theory

Abstract

Small ball inequalities have been extensively studied in the setting of Gaussian processes and associated Banach or Hilbert spaces. In this paper, we focus on studying small ball probabilities for sums or differences of independent, identically distributed random elements taking values in very general sets. Depending on the setting--abelian or nonabelian groups, or vector spaces, or Banach spaces--we provide a collection of inequalities relating different small ball probabilities that are sharp in many cases of interest. We prove these distribution-free probabilistic inequalities by showing that underlying them are inequalities of extremal combinatorial nature, related among other things to classical packing problems such as the kissing number problem. Applications are given to moment inequalities.