Exact upper and lower bounds on the difference between the arithmetic and geometric means

TL;DR

This paper derives exact upper and lower bounds for the difference between the expectation of a nonnegative random variable and its geometric mean, using variance and support-based measures.

Contribution

It provides novel, exact bounds on the difference between arithmetic and geometric means for nonnegative variables, in terms of variance and support measures.

Findings

Exact bounds are expressed via variance and support measures.

Bounds are tight and applicable to discrete and continuous distributions.

Results unify and extend classical inequalities.

Abstract

Let $X$ denote a nonnegative random variable with $\mathsf{E} X<\infty$. Upper and lower bounds on $\mathsf{E} X-\exp\mathsf{E}\ln X$ are obtained, which are exact, in terms of $V_X$ and $E_X$ for the upper bound and in terms of $V_X$ and $F_X$ for the lower bound, where $V_X:=\mathsf{Var}\sqrt X$, $E_X:=\mathsf{E}\big(\sqrt X-\sqrt{m_X}\,\big)^2$, $F_X:=\mathsf{E}\big(\sqrt{M_X}-\sqrt X\,\big)^2$, $m_X:=\inf S_X$, $M_X:=\sup S_X$, and $S_X$ is the support set of the distribution of $X$. Note that, if $X$ takes each of distinct real values $x_1,\dots,x_n$ with probability $1/n$, then $\mathsf{E} X$ and $\exp\mathsf{E}\ln X$ are, respectively, the arithmetic and geometric means of $x_1,\dots,x_n$.