关键词: measure concentration, infinitely divisible distribution, geometric distribution, Poisson distribution, Berry-Esseen theorem

Abstract: Let ${\cal I}$ be the set of all infinitely divisible random variables with finite second moments, ${\cal I}_0=\{X\in{\cal I}:{\rm Var}(X)>0\}$, $P_{\cal I}=\inf\limits_{X\in{\cal I}}P\{|X-E[X]|\le \sqrt{{\rm Var}(X)}\}$ and $P_{{\cal I}_0}=\inf\limits_{X\in{\cal I}_0} P\{|X-E[X]|< \sqrt{{\rm Var}(X)}\}$. Firstly, we prove that $P_{{\cal I}}\ge P_{{\cal I}_0}>0$. Secondly, we find the exact values of $\inf\limits_{X\in{\cal J}}P\{|X-E[X]|\le \sqrt{{\rm Var}(X)}\}$ and $\inf\limits_{X\in\cal J} P\{|X-E[X]|< \sqrt{{\rm Var}(X)}\}$ for the cases that $\cal J$ is the set of all geometric random variables, symmetric geometric random variables, Poisson random variables and symmetric Poisson random variables, respectively. As a consequence, we obtain that $P_{\cal I}\le {\rm e}^{-1}\sum\limits_{k=0}^{\infty}\frac{1}{2^{2k}(k!)^2}\approx 0.46576$ and $P_{{\cal I}_0}\le {\rm e}^{-1}\approx 0.36788$.

Key words: measure concentration, infinitely divisible distribution, geometric distribution, Poisson distribution, Berry-Esseen theorem