The Scientific World Journal

Volume 2014 (2014), Article ID 478612, 7 pages

http://dx.doi.org/10.1155/2014/478612

## Complete Moment Convergence and Mean Convergence for Arrays of Rowwise Extended Negatively Dependent Random Variables

^{1}College of Mathematics and Computer Science, Tongling University, Tongling 244000, China^{2}Department of Mathematics and Physics, Anhui Traditional Chinese Medical College, Hefei 230051, China

Received 29 August 2013; Accepted 19 December 2013; Published 5 February 2014

Academic Editors: S. Chen and T. Prieto-Rumeau

Copyright © 2014 Yongfeng Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The authors first present a Rosenthal inequality for sequence of extended negatively dependent (END) random variables. By means of the Rosenthal inequality, the authors obtain some complete moment convergence and mean convergence results for arrays of rowwise END random variables. The results in this paper extend and improve the corresponding theorems by Hu and Taylor (1997).

#### 1. Introduction

The concept of the complete convergence was introduced by Hsu and Robbins [1]. A sequence of random variables is said to converge completely to a constant if In view of the Borel-Cantelli lemma, the above result implies that almost surely. Therefore, the complete convergence is a very important tool in establishing almost sure convergence of summation of random variables as well as weighted sums of random variables.

Chow [2] presented the following more general concept of the complete moment convergence. Let be a sequence of random variables and , , and . If then the above result was called the complete moment convergence.

The following concept of negatively orthant dependent (NOD) random variables was introduced by Ebrahimi and Ghosh [3].

*Definition 1. *The random variables are said to be negatively upper orthant dependent (NUOD) if, for all real ,
and negatively lower orthant dependent (NLOD) if
Random variables are said to be NOD if they are both NUOD and NLOD.

Liu [4] extended the above negatively dependent structure and introduced the concept of extended negatively dependent (END) random variables.

*Definition 2. *We call random variables END if there exists a constant such that both
hold for each and all .

As described in Liu [4], the END structure is substantially more comprehensive than the NOD structure in that it can reflect not only a negative dependence structure but also a positive one, to some extent. Joag-Dev and Proschan [5] also pointed out that negatively associated (NA) random variables must be NOD and NOD is not necessarily NA. Since NOD implies END, NA random variables are END.

The convergence properties of NOD random sequences were studied in the different aspects. We refer reader to Taylor et al. [6] and Ko et al. [7, 8] for the almost sure convergence; Wu et al. [9] for the weak convergence and -convergence; Amini and Bozorgnia [10], Gan and Chen [11], Wu [12], Wu and Zhu [13], Qiu et al. [14], and Shen [15] for complete convergence; and Wu and Zhu [13] and Wu et al. [9] for complete moment convergence.

Since the paper of Liu [4] appeared, the probabilistic properties for END random variables have been studied by Chen et al. [16], Wu and Guan [17], and Qiu et al. [18]. Since NOD implies END and a great numbers of articles for NOD random variables have appeared in literature, it is very interesting to investigate convergence properties of this wider END class.

For a triangular array of rowwise independent random variables , we let be a sequence of positive real numbers with , and be a positive, even function such that
for some nonnegative integer . Conditions are given as
where is a positive integer.

Hu and Taylor [19] proved the following theorems.

Theorem A. *Let be an array of rowwise independent random variables and let satisfy (6) for some integer . Then (7), (8), and (9) imply
*

*Theorem B. Let be an array of rowwise independent random variables and let satisfy (6) for . Then conditions (7) and (8) imply (10).*

*Sung [20], Gan and Chen [21], and Wu and Zhu [13] extended Theorems A and B to the cases of -valued random elements, NA random variables, and NOD random variables, respectively. The goal of this paper is to study complete moment convergence and mean convergence for arrays of rowwise END random variables.*

*In this work, the authors first present a Rosenthal inequality for sequence of END random variables. By means of the Rosenthal inequality, the authors obtain the complete moment convergence result for arrays of rowwise END random variables, which extends and improves Theorems A and B. In addition, the authors study mean convergence for arrays of rowwise END random variables which was not considered by Hu and Taylor [19].*

*Throughout this paper, the symbol represents positive constants whose values may change from one place to another.*

*2. Main Results*

*Theorem 3. Let be an array of rowwise END random variables, and let be a sequence of positive real numbers with . Also, let be a positive, even function satisfying
for .(i)If , then conditions (7) and (8) imply
(ii)If , then conditions (7), (8), and
for imply (12).*

*Theorem 4. Let be an array of rowwise END random variables, and let be a sequence of positive real numbers with . Also, let be a positive, even function satisfying (11) for . (i)If , then (7) and
imply
(ii)If , (7), (14), and
imply (15).*

*Remark 5. *Since an independent random variable sequence is a special END sequence, Theorems 3 and 4 hold for arrays of rowwise independent random variables. Note that
implies (10). Therefore, the conclusion of Theorem 3 is stronger than those of Theorems A and B.

*3. Proofs*

*3. Proofs*

*To prove our main results, we need the following lemmas.*

*Lemma 6 (see [17]). Let be a sequence of END random variables with mean zero and . Let ; then there exists a constant such that
and .*

*Lemma 7. Let be a sequence of END random variables with mean zero and , where and . Let ; then
where is a positive constant depending only on .*

*Proof. *Let . Noting that
by taking in (18), we have
where
Letting , we can get (19) from (21). The proof is complete.

*Lemma 8 (see [4]). If random variables are END, then are still END, where are either all monotone increasing or all monotone decreasing.*

*Proof of Theorem 3. *Since
to prove (12), it is enough to prove that and . Note that (11) for implies
Following the methods used in the proofs of Theorems 1 and 2 in Gan and Chen [21], we can prove . Here we omit the details of the proofs. To prove (12), it suffices to show . Let
It follows from Lemma 8 that is an array of rowwise END random variables. Obviously
Hence
For , we have
By (11), (7), and (8), we have
Therefore, while is sufficiently large,
holds uniformly for . Then
Then we prove . We firstly consider it for the case (i). Let ; by (31), Lemma 7, and inequality, we have

By similar argument as in the proof of , we can get . For , by , as , (11), and (8), we have
For , since
we have
Let ; by , (11), and (8), we have

Secondly, we prove for the case (ii). By (31), Markov inequality, Lemma 7, and inequality, we have

For , we have
By similar argument as in the proof of and (replacing exponent 2 into ), we can get and . By similar argument as in the proof of , we can get .

For , by , we have
By , , and (13), we have

Then we prove . To start with, we consider it for the case . By , (11), and (8), we have
Secondly, we prove for the case . By (11) and (8), we have

Finally, we prove . From (11), we know as . Hence, we have
Therefore, while is sufficiently large,
holds uniformly for . By (44), , and similar argument as in the proof of , we can get
The proof is complete.

*Proof of Theorem 4. *Following the notations of the proof in Theorem 3. To start with, we prove (15) for the case . For all ,

Without loss of generality we may assume . By Markov inequality, (11), and (14), we have
From (11), (7), and (14), we have
Therefore, while is sufficiently large, for , we have (31). Let ; by (31), Lemma 7, and inequality, we have
By similar argument as in the proof of , we can prove
For , since
we have
Therefore, by similar argument as in the proof of , we can prove
By similar argument as in the proof of , we can prove .

The proof of (15) for the case is similar to that of (ii) in Theorem 3, so we omit the details. The proof is complete.

*Conflict of Interests*

*Conflict of Interests*

*The authors declare that there is no conflict of interests regarding the publication of this paper.*

*Acknowledgments*

*Acknowledgments*

*The authors are grateful to the referees for carefully reading the paper and for providing some comments and suggestions which improved the paper. This work was supported by the Humanities and Social Sciences Foundation for the Youth Scholars of Ministry of Education of China (no. 12YJCZH217), the Natural Science Foundation of Anhui Province (no. 1308085MA03), and the National Natural Science Foundation of China (no. 11201001).*

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