A Generalization of the Petrov Strong Law of Large Numbers

TL;DR

This paper generalizes Petrov's strong law of large numbers, extending its applicability to dependent and independent random variables with arbitrary normalization sequences, broadening the theoretical understanding of convergence conditions.

Contribution

It introduces a generalized version of Petrov's law, applicable to dependent variables and arbitrary norming sequences, expanding the classical results.

Findings

Extended the strong law to dependent variables

Allowed arbitrary norming sequences in the law

Broadened the conditions for almost sure convergence

Abstract

In 1969 V.V.~Petrov found a new sufficient condition for the applicability of the strong law of large numbers to sequences of independent random variables. He proved the following theorem: let $\{X_{n}\}_{n=1}^{\infty}$ be a sequence of independent random variables with finite variances and let $S_{n}=\sum_{k=1}^{n} X_{k}$. If $Var (S_{n})=O (n^{2}/\psi(n))$ for a positive non-decreasing function $\psi(x)$ such that $\sum 1/(n \psi(n)) < \infty$ (Petrov's condition) then the relation $(S_{n}-ES_{n})/n \to 0$ a.s. holds. In 2008 V.V.~Petrov showed that under some additional assumptions Petrov's condition remains sufficient for the applicability of the strong law of large numbers to sequences of random variables without the independence condition. In the present work, we generalize Petrov's results (for both dependent and independent random variables), using an arbitrary norming sequence in place of the classical normalization.