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The Scientific World Journal
Volume 2014, Article ID 478612, 7 pages
http://dx.doi.org/10.1155/2014/478612
Research Article

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

1College of Mathematics and Computer Science, Tongling University, Tongling 244000, China
2Department 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

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

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

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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