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Discrete Dynamics in Nature and Society

Volume 2012 (2012), Article ID 562838, 8 pages

http://dx.doi.org/10.1155/2012/562838

## Strong Convergence Properties for Asymptotically Almost Negatively Associated Sequence

^{1}School of Mathematics and Computational Science, Anqing Teachers College, Anqing 246133, China^{2}College of Water Conservancy and Hydropower Engineering, HoHai University, Nanjing 210098, China^{3}College of Mathematics and Computation Science, Anhui Normal University, Wuhu 241000, China

Received 22 June 2012; Accepted 10 September 2012

Academic Editor: Garyfalos Papaschinopoulos

Copyright © 2012 Xueping Hu 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

By applying the moment inequality for asymptotically almost negatively associated (in short *AANA*) random sequence and truncated method, we get the three series theorems for *AANA* random variables. Moreover, a strong convergence property for the partial sums of *AANA* random sequence is obtained. In addition, we also study strong convergence property for weighted sums of *AANA* random sequence.

#### 1. Introduction

A finite family of random variables is said to be negatively associated (in short *NA*) if for every pair of disjoint subsets of
whenever are coordinate-wise nondecreasing such that the covariance exists. An infinite sequence of random variables is said to be *NA* if every finite subfamily is *NA*.

The notion of *NA* was first introduced by Block et al. (1982) [1]. Joag-Dev and Proschan (1983) [2] showed that many well-known multivariate distributions possess the *NA* property. By inspecting the proof of maximal inequality for *NA* random variables in Matuła [3], Chandra and Ghosal discovered that one can also allow negative correlations provided they are small. Primarily motivated by this, Chandra and Ghosal [4, 5] introduced the following dependence.

*Definition 1.1. *A sequence of random variables is said to be asymptotically almost negatively associated, if there exists a nonnegative sequence as such that
for all and for all coordinatewise nondecreasing continuous functions and whenever the variances exit.

Obviously, the family of *AANA* sequences contain *NA* (in particular, independent) sequences (with ) and some more sequences of random variables which are not much deviated from being *NA*. An example of an *AANA* sequence which is not *NA* was introduced by Chandra and Ghosal [4].

Since the notion of *AANA* sequence was introduced by Chandra and Ghosal [4], the *AANA* properties have aroused wide interest because of numerous applications in reliability theory, percolation theory, and multivariate statistical analysis. In the past decades, a lot of effort was dedicated to proving the limit theorems of *AANA* random variables; we can refer to [4–10]. Hence, extending the limit properties of *AANA* random variables has very important significance in the theory and application.

In this paper, we mainly study the strong convergence property for the partial sums of *AANA* random variables; furthermore the strong convergence property for weighted sums of *AANA* random variables is also obtained.

Throughout the paper, let be the indicator function of the set , and let for some . The denotes that there exits a positive constant such that . The symbol represents a positive constant which may be different in various places. The main results of this paper are dependent on the following lemmas.

Lemma 1.2 (Yuan and An [6]). *Let be a sequence of AANA random variables with mixing coefficients , and let be all nondecreasing (or nonincreasing) functions; then is still a sequence of AANA random variables with mixing coefficients .*

Lemma 1.3 (Wang et al. [7]). *For , let be a sequence of AANA random variables with mixing coefficients and for each . If , then there exists a positive constant depending only on such that
**
for all where , and is the dual number of .*

Lemma 1.4 (Wu [11]). *Let be a sequence of random variables. For each , there exists a random variable such that
**
then, for any , the following two statements hold:
*

Lemma 1.5 (Sung [12]). *Let be a positive increasing function on satisfying as , and let be the inverse function of . If and satisfy, respectively,
**
then
*

#### 2. Strong Convergence for the Partial Sums of *AANA* Random Variables

Theorem 2.1. *Let be a sequence of AANA random variables with , if the following assumptions holds:
**
then almost surely convergence.*

*Remark 2.2. *The proof of Theorem 2.1 is similar to the proof of Theorem in [11], and by Lemmas 1.2 and 1.3, we omit it.

Theorem 2.3. *Let be a sequence of AANA random variables with .**Assume that is a sequence of even functions in , for each , is a positive nondecreasing function in and satisfies one of the following conditions:*(i)*for there exists a constant such that ;*(ii)*for , there exists a constant and such that ; however, for , furthermore assume that , for each .**
Let be a constant sequence satisfying such that
**
then almost surely convergence, and further it follows from the “Kronecker lemma” that
*

*Proof. *For each , denote .

By Lemma 1.2, we can see that, for fixed , is still a sequence of *AANA* random variables. To verity the Theorem 2.3, for we only need to prove the convergence of three series of (2.1) under condition (i) or (ii). The proof of Theorem 2.3 includes the following three steps.

(1) *We prove ** under condition (i) or (ii). *

For each , if satisfies condition (i), noting that is a positive nondecreasing even function in , it is obvious that
By (2.2), we can get
If satisfies condition (ii), it is easy to prove that (2.5) also holds when .

(2) *Next we will show *.

If satisfies condition (i), it follows that
On the other hand, if condition (ii) holds, according to , for each , we have
Hence, it follows from (2.2) that

(3) Finally we prove .

If satisfies condition (i), for each , it is easy to show that by the inequality
If condition (ii) holds, according to the inequality, for each , we get
Therefore, it also follows from (2.2) that
The proof of the Theorem 2.3 is completed by (2.5), (2.8), and (2.11).

Corollary 2.4. *Let be a sequence of AANA random variables with , and let be a constant sequence satisfying . For , let , and if satisfies (2.2), then a.s., as .*

*Proof. *It is easy to check that is a sequence of even functions in , for each , is a positive nondecreasing function in, and the following condition holds:

#### 3. Strong Convergence for the Weighted Sums of *AANA* Random Variables

Theorem 3.1. *Let be a different distribution sequence of AANA random variables with and , for each . There exists a random variable satisfying , such that
**
Assume that the following conditions hold for the constant arrays .**(i) ; (ii) for some constant , , where satisfy Lemma 1.5; then
*

*Proof. *Let :
It suffices to prove that a.s., as . We will estimate each of these terms separately.

To verity a.s., as , we can get from (3.1) and that
Hence, by the Borel-Cantelli Lemma it is obvious that a.s., as .

Next we will show that *，* as almost surely. For any , note that , and it follows from the Markov inequality, Lemma 1.2*,* Lemma 1.3*, *inequality, and Lemma 1.5 that
the last series converges using condition (ii), and by Borel-Cantelli lemma we get a.s., as .

Finally we will prove that a.s., as . Note that ; for each , it is easy to show that by Lemma 1.5, Lemma 1.4, and theKronecker lemma
The proof of Theorem 3.1 is completed.

#### Acknowledgments

This paper is supported by the National Natural Science Foundantion of China (10901003) and the Natural Science Foundation of Anhui Province (KJ2012ZD001, KJ2013A126, KJ2012Z233).

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