The sharp form of the Kolmogorov--Rogozin inequality and a conjecture of Leader--Radcliffe

TL;DR

This paper establishes an optimal bound for the concentration function of sums of independent variables, resolving a longstanding question and characterizing extremal distributions as mixtures of uniform distributions on arithmetic progressions.

Contribution

It provides the sharp form of the Kolmogorov-Rogozin inequality and characterizes the extremal distributions, settling a conjecture from 1994.

Findings

Derived an optimal bound for the concentration function of sums.

Characterized extremal distributions as mixtures of uniform distributions.

Resolved a 1994 conjecture by Leader and Radcliffe.

Abstract

Let $X$ be a random variable and define its concentration function by $$\mathcal{Q}_{h}(X)=\sup_{x\in \mathbb{R}}\mathbb{P}(X\in (x,x+h]).$$ For a sum $S_n=X_1+\cdots+X_n$ of independent real-valued random variables the Kolmogorov-Rogozin inequality states that $$\mathcal{Q}_{h}(S_n)\leq C\left(\sum_{i=1}^{n}(1-\mathcal{Q}_{h}(X_i))\right)^{-\frac{1}{2}}.$$ In this paper we give an optimal bound for $\mathcal{Q}_{h}(S_n)$ in terms of $\mathcal{Q}_{h}(X_i)$, which settles a question posed by Leader and Radcliffe in 1994. Moreover, we show that the extremal distributions are mixtures of two uniform distributions each lying on an arithmetic progression.