Complete moment and integral convergence for sums of negatively associated random variables

TL;DR

This paper extends classical complete convergence theorems to sums of negatively associated random variables, providing necessary and sufficient moment conditions for convergence and establishing equivalence with integral convergence.

Contribution

It introduces refined moment conditions for complete convergence of negatively associated variables, extending previous results from i.i.d. to dependent cases.

Findings

Provides necessary and sufficient conditions for complete moment convergence.

Extends classical theorems from i.i.d. to negatively associated variables.

Shows equivalence between complete moment and integral convergence.

Abstract

For a sequence of identically distributed negatively associated random variables $\{X_n; n\geq 1\}$ with partial sums $S_n=\sum_{i=1}^nX_i, n\geq 1$, refinements are presented of the classical Baum-Katz and Lai complete convergence theorems. More specifically, necessary and sufficient moment conditions are provided for complete moment convergence of the form $$ \sum_{n \ge n_0} n^{r -2 -\frac{1}{pq}} a_n E(\max_{1 \le k \le n}|S_k|^{\frac{1}{q}} - \epsilon b_n^{\frac{1}{pq}})^+ < \infty $$ to hold where $r>1, q>0$ and either $n_0=1, 0<p<2, a_n=1, b_n=n$ or $n_0=3, p=2, a_n=(\log n)^{-\frac{1}{2q}}, b_n=n\log n$. These results extend results of Chow (1988) and Li and Sp\u{a}taru (2005) from the independent and identically distributed case to the identically distributed negatively associated setting. The complete moment convergence is also shown to be equivalent to a form of complete integral convergence.