On general strong laws of large numbers for fields of random variables

TL;DR

This paper introduces a new probabilistic maximal inequality for random fields, extending existing methods to establish strong laws of large numbers under broad dependence conditions without moment assumptions.

Contribution

It develops a H"ajek-Renyi type maximal inequality for random fields using probabilities, generalizing previous results for sequences and applying to strong laws without moment constraints.

Findings

Established a probabilistic maximal inequality for random fields.

Proved strong laws of large numbers under general dependence.

Applied results to logarithmically weighted sums without moment assumptions.

Abstract

A general method to prove strong laws of large numbers for random fields is given. It is based on the H\'ajek - R\'enyi type method presented in Nosz\'aly and T\'om\'acs \cite{noszaly} and in T\'om\'acs and L\'ibor \cite{thomas06}. Nosz\'aly and T\'om\'acs \cite{noszaly} obtained a H\'ajek-R\'enyi type maximal inequality for random fields using moments inequalities. Recently, T\'om\'acs and L\'ibor \cite{thomas06} obtained a H\'ajek-R\'enyi type maximal inequality for random sequences based on probabilities, but not for random fields. In this paper we present a H\'ajek-R\'enyi type maximal inequality for random fields, using probabilities, which is an extension of the main results of Nosz\'aly and T\'om\'acs \cite{noszaly} by replacing moments by probabilities and a generalization of the main results of T\'om\'acs and L\'ibor \cite% {thomas06} for random sequences to random fields. We apply our results to establishing a logarithmically weighted sums without moment assumptions and under general dependence conditions for random fields.