- Research Article
- Open access
- Published:

# On Strong Law of Large Numbers for Dependent Random Variables

*Journal of Inequalities and Applications*
**volumeÂ 2011**, ArticleÂ number:Â 279754 (2011)

## Abstract

We discuss strong law of large numbers and complete convergence for sums of uniformly bounded negatively associate (NA) random variables (RVs). We extend and generalize some recent results. As corollaries, we investigate limit behavior of some other dependent random sequence.

## 1. Introduction

Throughout this paper, let denote the set of nonnegative integer, let be a sequence of random variables defined on probability space (), and put . The symbol will denote a generic constant () which is not necessarily the same one in each appearance.

In [1], Jajte studied a large class of summability method as follows: a sequence is summable to by the method () if

The main result of Jajte is as follows.

Theorem 1.1.

Let be a positive, increasing function and a positive function such that satisfies the following conditions.

(1)For some , is strictly increasing on with range .

(2)There exist C and a positive integer such that , .

(3)There exist constants and such that , .

Then, for i.i.d. random variables ,

where is the inverse of function , is the indicator of event .

Motivated by Jajte [1], the present paper is devoted to the study of the limiting behavior of sums when are dependent RVs In particular, we willl consider the case when are NA RVs and obtain some general results on the complete convergence of dependent RVs First, we shall give some definitions.

Definition 1.2.

A finite family of random variables is said to be negatively associated (abbreviated NA) if, for every pair of disjoint subsets and of , we have

whenever and are coordinatewise increasing and the covariance exists.

Definition 1.3.

A finite family of random variables is said to be positively associated (abbreviated PA) if

whenever and are coordinatewise increasing and the covariance exists.

An infinite family of random variables is NA (resp., PA) if every finite subfamily is NA (resp., PA).

Let be the -algebra generated by RVs,â€‰â€‰.

Definition 1.4.

A sequence of random variables is said to be -dependence if and are independent for all and such that .

Definition 1.5.

A sequence of random variables is said to be -mixing (or uniformly strong mixing), if

These concepts of dependence were introduced by Esary et al. [2] and Joag-Dev and Proschan [3]. Their basic properties may be found in [2, 3] and the references therein.

Definition 1.6.

Let be a sequence of random variables which is said to be: uniformly bounded by a random variable (we write ) if there exists a constant , for almost every , such that

Remark 1.7.

The uniformly bounded random variables in (1.6) can be insured by moment conditions. For example, if

then there exists a uniformly bounded random variable such that .

The structure of this paper is as follows. Some needed technical results will be presented in Section 2. The strong law of large numbers for NA RVs will be established in Section 3. The Spitzer and Hus-Robbins-type law of large numbers will be presented in Sections 4 and 5, respectively.

## 2. Preliminaries

We now present some terminologies and lemmas. The following six properties are listed for reference in obtaining the main results in the next sections. Detailed proofs can be founded in the cited references.

Lemma 2.1 (cf. [4]) (three-series theorem for NA).

Let â€‰â€‰be NA. Let and let . In order that converges a.s., it is sufficient that

(1),

(2) converges,

(3).

Lemma 2.2 (cf. [4]).

Let be NA with , , then for , for all

Lemma 2.3 (cf. [5]).

Let be -dependence with , , then

Lemma 2.4 (cf. [5]).

Let be -mixing with , , then

Lemma 2.5 (cf. [6]).

Let be PA with , , then

Furthermore, if

then

where .

Lemma 2.6.

Let be a sequence of random variables and a random variable. If , then for all ,

Proof.

By the integral equality

it follows that

Lemma 2.7 (cf. [7]).

Let be a sequence of events defined on . If , then , if and for , then .

## 3. Strong Law of Large Numbers

Theorem 3.1.

Let , and be as in Theorem 1.1, and let be a sequence of negatively associated random variables with . Assume that . If , then

Conversely, let be a sequence of identically distributed NA random variables, if (3.1) is true, then .

Proof.

Assume that . To prove (3.1) by applying the Kronecker lemma, it suffices to show that

Here, we shall use the three-series theorem for NA RVs.

Let . Then, by , we have

which shows that , and

Therefore, from , it follows that

To this end we estimate the series

Conversely,since are identically NA RVs. If (3.1) holds, that is,

It follows that

which shows that . Hence,

where and .

Since is still an NA sequence. Defining the following events,

we have , and , for . By Lemma 2.7, if a.s., then and . Therefore,

which is equivalent to .

These complete the proof of Theorem 3.1.

Theorem 3.1 also includes a particular case of logarithmic means, we can establish the following.

Corollary 3.2.

Let be a sequence of NA RVs with and . If , then, one has

Proof.

Let , , that is, . In this case, as , therefore , for .

Corollary 3.3.

Let be a sequence of NA RVs with and . If , then, for every , , one has

Remark 3.4.

As pointed out by Jajte [1], Theorem 3.1 includes several regular summability methods such as (1) the Kolmogorov SLLN ; (2) the classical MZ SLLN .

## 4. Spitzer Type Law of Large Numbers

Since the definition of complete convergence was introduced by Hsu and Robins, there have been many authors who devote themselves to the study of the complete convergence for sums of independent and dependent RVs and obtain a series of elegant results, see [4, 8] and reference therein.

We say that the Hsu-Robbins [9] law of large numbers (LLN) is valid if, for all ,

and the Spitzer [10] LLN is valid if, for all ,

Theorem 4.1.

Let be as in Theorem 1.1, and let be a sequence of NA random variables with . Assume that . If , then for all ,

Conversely, let be a sequence of identically distributed NA random variables, if (4.3) is true, then and .

Proof.

For , let , , then for every

Note that

For the first term on the RHS of (4.5), by Markov inequality and Lemma 2.2 and (3.6), we have

For the second term on the RHS of (4.5), since

hence,

For the third term on the RHS of (4.5), we have, by (3.5),

Therefore, (4.3) follows.

Conversely, since imply that ., hence from Theorem 3.1, we have . These complete the proof of Theorem 4.1.

Corollary 4.2.

Under the assumptions of Theorem 4.1, one has

Proof.

Denote , and noticing that

hence, similarly to the proof of Theorem 4.1, we obtain (4.10).

Analogously, we can prove the following corollaries, and omit the details.

Corollary 4.3.

Let be a sequence of -mixing random variables with . Assume that . If , and

then (4.3) holds.

Corollary 4.4.

Let be a sequence of -dependent random variables with . Assume that . If , then (4.3) holds.

Corollary 4.5.

Let be a sequence of PA random variables with . Assume that . If , and

then (4.3) holds.

## 5. Hsu-Robbins Type Law of Large Numbers

Theorem 5.1.

Let , be define as in Theorem 1.1, but the following condition (3) is replaced by

(3â€²) There exist constants such that , , , and let be a sequence of NA random variables with . Assume that . If , then for all ,

Proof.

From the previous section, we know that to prove Theorem 5.1, we need only to prove the convergence of the following three series.

First, note that , we have

Hence, as . Next, by (4.4), we have

Last, from the definition of and the NA's property, we know that remains a sequence of NA RVs. By applying Lemma 2.2 and inequality, we have

It is easy to see that

These complete the proof of Theorem 5.1.

## References

Jajte R:

**On the strong law of large numbers.***The Annals of Probability*2003,**31**(1):409â€“412.Esary JD, Proschan F, Walkup DW:

**Association of random variables, with applications.***Annals of Mathematical Statistics*1967,**38:**1466â€“1474. 10.1214/aoms/1177698701Joag-Dev K, Proschan F:

**Negative association of random variables, with applications.***The Annals of Statistics*1983,**11**(1):286â€“295. 10.1214/aos/1176346079Su C, Wang YB:

**Strong convergence for identically distributed negatively associated sequences.***Chinese Journal of Applied Probability and Statistics*1998,**14**(2):131â€“140.Sunklodas J:

**On the law of large numbers for weakly dependent random variables.***Lietuvos Matematikos Rinkinys*2004,**44**(3):359â€“371.Yang SC:

**Moment ineqaulities for partial sums of random variables.***Science in China Series A*2003,**44**(3):218â€“223.MatuÅ‚a P:

**A note on the almost sure convergence of sums of negatively dependent random variables.***Statistics & Probability Letters*1992,**15**(3):209â€“213. 10.1016/0167-7152(92)90191-7Bingyi J, Liang HY:

**Strong limit theorems for weighted sums of negatively associated random sequence.***Journal of Theoretical Probability*2008,**21**(4):890â€“909. 10.1007/s10959-007-0128-4Hsu PL, Robbins H:

**Complete convergence and the law of large numbers.***Proceedings of the National Academy of Sciences of the United States of America*1947,**33:**25â€“31. 10.1073/pnas.33.2.25Spitzer F:

**A combinatorial lemma and its application to probability theory.***Transactions of the American Mathematical Society*1956,**82:**323â€“339. 10.1090/S0002-9947-1956-0079851-X

## Acknowledgments

This work is supported by the National Nature Science Foundation of China (no. 11071104) and the Anhui high Education Research Grant (no. KJ2010A337). The author expresses his sincere gratitude to the referees and the editors for their hospitality.

## Author information

### Authors and Affiliations

### Corresponding author

## Rights and permissions

**Open Access** This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## About this article

### Cite this article

Wang, Z. On Strong Law of Large Numbers for Dependent Random Variables.
*J Inequal Appl* **2011**, 279754 (2011). https://doi.org/10.1155/2011/279754

Received:

Accepted:

Published:

DOI: https://doi.org/10.1155/2011/279754