Complete convergence is studied for linear statistics that are weighted sums of identically distributed ρ * -mixing random variables under a suitable moment condition. The results obtained generalize and complement some earlier results. A Marcinkiewicz-Zygmund-type strong law is also obtained

Chang-Chun Tan

Jin-Guan Lin

Xing-Cai Zhou

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Hindawi Publishing Corporation
Journal of Inequalities and Applications
Volume 2011, Article ID 157816, 8 pages
doi:10.1155/2011/157816
Research Article
On the Strong Laws for Weighted Sums of
ρ∗-Mixing Random Variables
Xing-Cai Zhou,1, 2 Chang-Chun Tan,3 and Jin-Guan Lin1
1 Department of Mathematics, Southeast University, Nanjing 210096, China
2 Department of Mathematics and Computer Science, Tongling University, Tongling, Anhui 244000, China
3 School of Mathematics, Heifei University of Technology, Hefei, Anhui 230009, China
Correspondence should be addressed to Chang-Chun Tan, cctan@ustc.edu.cn
Received 26 October 2010; Revised 5 January 2011; Accepted 27 January 2011
Academic Editor: Matti K. Vuorinen
Copyright q 2011 Xing-Cai Zhou 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.
Complete convergence is studied for linear statistics that are weighted sums of identically
distributed ρ∗-mixing random variables under a suitable moment condition. The results obtained
generalize and complement some earlier results. A Marcinkiewicz-Zygmund-type strong law is
also obtained.
1. Introduction
Suppose that {Xn; n ≥ 1} is a sequence of random variables and S is a subset of the natural
number set N. Let FS = σ(Xi; i ∈ S),
ρ∗n = sup
{
corr
(
f, g
)
: ∀S × T ⊂ N ×N, dist(S, T) ≥ n, ∀f ∈ L2(FS), g ∈ L2(FT )
}
, (1.1)
where
corr
(
f, g
)
=
Cov
{
f(Xi; i ∈ S), g
(
Xj ; j ∈ T
)}
[
Var
{
f(Xi; i ∈ S)
}
Var
{
g
(
Xj ; j ∈ T
)}]1/2 . (1.2)
Definition 1.1. A random variable sequence {Xn; n ≥ 1} is said to be a ρ∗-mixing random
variable sequence if there exists k ∈ N such that ρ∗
k
< 1.
2 Journal of Inequalities and Applications
The notion of ρ∗-mixing seems to be similar to the notion of ρ-mixing, but they
are quite different from each other. Many useful results have been obtained for ρ∗-mixing
random variables. For example, Bradley [1] has established the central limit theorem, Byrc
and Smoleński [2] and Yang [3] have obtained moment inequalities and the strong law
of large numbers, Wu [4, 5], Peligrad and Gut [6], and Gan [7] have studied almost sure
convergence, Utev and Peligrad [8] have established maximal inequalities and the invariance
principle, An and Yuan [9] have considered the complete convergence and Marcinkiewicz-
Zygmund-type strong law of large numbers, and Budsaba et al. [10] have proved the rate of
convergence and strong law of large numbers for partial sums of moving average processes
based on ρ−-mixing random variables under some moment conditions.
For a sequence {Xn; n ≥ 1} of i.i.d. random variables, Baum and Katz [11] proved
the following well-known complete convergence theorem: suppose that {Xn; n ≥ 1} is a
sequence of i.i.d. random variables. Then EX1 = 0 and E|X1|rp < ∞ (1 ≤ p < 2, r ≥ 1) if and
only if
∑∞
n=1 n
r−2P(|∑ni=1 Xi| > n1/pε) < ∞ for all ε > 0.
Hsu and Robbins [12] and Erdös [13] proved the case r = 2 and p = 1 of the above
theorem. The case r = 1 and p = 1 of the above theorem was proved by Spitzer [14]. An and
Yuan [9] studied the weighted sums of identically distributed ρ∗-mixing sequence and have
the following results.
Theorem B. Let {Xn; n ≥ 1} be a ρ∗-mixing sequence of identically distributed random variables,
αp > 1, α > 1/2, and suppose that EX1 = 0 for α ≤ 1. Assume that {ani; 1 ≤ i ≤ n} is an array of
real numbers satisfying
n∑
i=1
|ani|p = O(δ), 0 < δ < 1, (1.3)
Ank = 
{
1 ≤ i ≤ n : |ani|p > (k + 1)−1
}
≥ ne−1/k. (1.4)
If E|X1|p < ∞, then
∞∑
n=1
nαp−2P
(
max
1≤j≤n
∣∣∣∣∣
j∑
i=1
aniXi
∣∣∣∣∣ > εn
α
)
< ∞. (1.5)
Theorem C. Let {Xn; n ≥ 1} be a ρ∗-mixing sequence of identically distributed random variables,
αp > 1, α > 1/2, and EX1 = 0 for α ≤ 1. Assume that {ani; 1 ≤ i ≤ n} is array of real numbers
satisfying (1.3). Then
n−1/p
n∑
i=1
aniXi −→ 0 a.s. (n −→ ∞). (1.6)
Recently, Sung [15] obtained the following complete convergence results for weighted
sums of identically distributed NA random variables.
Journal of Inequalities and Applications 3
Theorem D. Let {X,Xn; n ≥ 1} be a sequence of identically distributed NA random variables, and
let {ani; 1 ≤ i ≤ n, n ≥ 1} be an array of constants satisfying
Aα = lim sup
n→∞
Aα,n < ∞, Aα,n =
n∑
i=1
|ani|α
n
(1.7)
for some 0 < α ≤ 2. Let bn = n1/α(logn)1/γ for some γ > 0. Furthermore, suppose that EX = 0 where
1 < α ≤ 2. If
E|X|α < ∞, for α > γ,
E|X|α log|X| < ∞, for α = γ,
E|X|γ < ∞, for α < γ,
(1.8)
then
∞∑
n=1
1
n
P
(
max
1≤j≤n
∣∣∣∣∣
j∑
i=1
aniXi
∣∣∣∣∣ > bnε
)
< ∞ ∀ε > 0. (1.9)
We find that the proof of Theorem C is mistakenly based on the fact that (1.5) holds for
αp = 1. Hence, the Marcinkiewicz-Zygmund-type strong laws for ρ∗-mixing sequence have
not been established.
In this paper, we shall not only partially generalize Theorem D to ρ∗-mixing case, but
also extend Theorem B to the case αp = 1. Themain purpose is to establish theMarcinkiewicz-
Zygmund strong laws for linear statistics of ρ∗-mixing random variables under some suitable
conditions.
We have the following results.
Theorem 1.2. Let {X,Xn;n ≥ 1} be a sequence of identically distributed ρ∗-mixing random variables,
and let {ani; 1 ≤ i ≤ n, n ≥ 1} be an array of constants satisfying
Aβ = lim sup
n→∞
Aβ,n < ∞, Aβ,n =
n∑
i=1
|ani|β
n
, (1.10)
where β = max(α, γ) for some 0 < α ≤ 2 and γ > 0. Let bn = n1/α(logn)1/γ . If EX = 0 for 1 < α ≤ 2
and (1.8) for α/= γ , then (1.9) holds.
Remark 1.3. The proof of Theorem D was based on Theorem 1 of Chen et al. [16], which gave
sufficient conditions about complete convergence for NA random variables. So far, it is not
known whether the result of Chen et al. [16] holds for ρ∗-mixing sequence. Hence, we use
different methods from those of Sung [15]. We only extend the case α/= γ of Theorem D to
ρ∗-mixing random variables. It is still open question whether the result of Theorem D about
the case α = γ holds for ρ∗-mixing sequence.
4 Journal of Inequalities and Applications
Theorem 1.4. Under the conditions of Theorem 1.2, the assumptions EX = 0 for 1 < α ≤ 2 and (1.8)
for α/= γ imply the following Marcinkiewicz-Zygmund strong law:
b−1n
n∑
i=1
aniXi −→ 0 a.s. (n −→ ∞). (1.11)
2. Proof of the Main Result
Throughout this paper, the symbol C represents a positive constant though its value may
change from one appearance to next. It proves convenient to define logx = max(1, lnx),
where lnx denotes the natural logarithm.
To obtain our results, the following lemmas are needed.
Lemma 2.1 (Utev and Peligrad [8]). Suppose N is a positive integer, 0 ≤ r < 1, and q ≥ 2. Then
there exists a positive constant D = D(N, r, q) such that the following statement holds.
If {Xi; i ≥ 1} is a sequence of random variables such that ρ∗N ≤ r with EXi = 0 and E|Xi|q <
∞ for every i ≥ 1, then for all n ≥ 1,
E
(
max
1≤i≤n
|Si|q
)
≤ D
⎛
⎝
n∑
i=1
E|Xi|q +
(
n∑
i=1
EX2i
)q/2⎞
⎠, (2.1)
where Si =
∑i
j=1 Xj .
Lemma 2.2. Let X be a random variable and {ani; 1 ≤ i ≤ n, n ≥ 1} be an array of constants
satisfying (1.10), bn = n1/α(logn)
1/γ . Then
∞∑
n=1
n−1
n∑
i=1
P(|aniX| > bn) ≤
⎧
⎪⎨
⎪⎩
CE|X|α for α > γ,
CE|X|γ for α < γ.
(2.2)
Proof. If γ > α, by
∑n
i=1 |ani|γ = O(n) and Lyapounov’s inequality, then
1
n
n∑
i=1
|ani|α ≤
(
1
n
n∑
i=1
|ani|γ
)α/γ
= O(1). (2.3)
Hence, (1.7) is satisfied. From the proof of (2.1) of Sung [15], we obtain easily that the result
holds.
Journal of Inequalities and Applications 5
Proof of Theorem 1.2. Let Xni = aniXiI(|aniXi| ≤ bn). For all ε > 0, we have
∞∑
n=1
1
n
P
(
max
1≤j≤n
∣∣∣∣∣
j∑
i=1
aniXi
∣∣∣∣∣ > εbn
)
≤
∞∑
n=1
1
n
P
(
max
1≤j≤n
∣∣anjXj
∣∣ > bn
)
+
∞∑
n=1
1
n
P
(
max
1≤j≤n
∣∣∣∣∣
j∑
i=1
Xni
∣∣∣∣∣ > εbn
)
:= I1 + I2.
(2.4)
To obtain (1.9), we need only to prove that I1 < ∞ and I2 < ∞.
By Lemma 2.2, one gets
I1 ≤
∞∑
n=1
1
n
n∑
j=1
P
(∣∣anjXj
∣∣ > bn
)
=
∞∑
n=1
1
n
n∑
j=1
P
(∣∣anjX
∣∣ > bn
)
< ∞. (2.5)
Before the proof of I2 < ∞, we prove firstly
b−1n max1≤j≤n
∣∣∣∣∣
j∑
i=1
EaniXiI(|aniXi| ≤ bn)
∣∣∣∣∣ −→ 0, as n −→ ∞. (2.6)
For 0 < α ≤ 1,
b−1n max1≤j≤n
∣∣∣∣∣
j∑
i=1
EaniXiI(|aniXi| ≤ bn)
∣∣∣∣∣ ≤ b
−1
n
n∑
i=1
E|aniXi|I(|aniXi| ≤ bn) ≤ b−αn
n∑
i=1
|ani|αE|X|α
≤ C(logn)−α/γE|X|α −→ 0, as n −→ ∞.
(2.7)
For 1 < α ≤ 2,
b−1n max1≤j≤n
∣∣∣∣∣
j∑
i=1
EaniXiI(|aniXi| ≤ bn)
∣∣∣∣∣ = b
−1
n max1≤j≤n
∣∣∣∣∣
j∑
i=1
EaniXiI(|aniXi| > bn)
∣∣∣∣∣(EXi = 0)
≤ b−1n
n∑
i=1
E|aniXi|I(|aniXi| > bn) ≤ b−αn
n∑
i=1
|ani|αE|X|α
≤ C(logn)−α/γE|X|α −→ 0, as n −→ ∞.
(2.8)
Thus (2.6) holds. So, to prove I2 < ∞, it is enough to show that
I3 =
∞∑
n=1
1
n
P
(
max
1≤j≤n
∣∣∣∣∣
j∑
i=1
Xni − EXni
∣∣∣∣∣ > εbn
)
< ∞, ∀ε > 0. (2.9)
6 Journal of Inequalities and Applications
By the Chebyshev inequality and Lemma 2.1, for q ≥ max{2, γ}, we have
I3 ≤ C
∞∑
n=1
n−1b−qn E
⎛
⎝max
1≤j≤n
∣∣∣∣∣
j∑
i=1
Xni − EXni
∣∣∣∣∣
q
⎞
⎠
≤ C
∞∑
n=1
n−1b−qn
n∑
i=1
E|aniXi|qI(|aniXi| ≤ bn)
+ C
∞∑
n=1
n−1b−qn
[
n∑
i=1
E(aniXi)2I(|aniXi| ≤ bn)
]q/2
:= I31 + I32.
(2.10)
For I31, we consider the following two cases.
If α < γ , note that E|X|γ < ∞. We have
I31 ≤ C
∞∑
n=1
n−1b−γn
n∑
i=1
|ani|γE|X|γ ≤ C
∞∑
n=1
n
−
γ
α
(
logn
)−1
< ∞. (2.11)
If α > γ , note that E|X|α < ∞. we have
I31 ≤ C
∞∑
n=1
n−1b−αn
n∑
i=1
|ani|αE|X|α ≤ C
∞∑
n=1
n−1
(
logn
)−α/γ
< ∞. (2.12)
Next, we prove I32 < ∞ in the following two cases.
If α < γ ≤ 2 or γ < α ≤ 2, take q > max(2, 2γ/α). Noting that E|X|α < ∞, we have
I32 ≤ C
∞∑
n=1
n−1b−αq/2n
[
n∑
i=1
|ani|αE|X|α
]q/2
≤ C
∞∑
n=1
n−1
(
logn
)−αq/(2γ)
< ∞.
(2.13)
If γ > 2 ≥ α or γ ≥ 2 > α, one gets E|X|2 < ∞. Since ∑ni=1 |ani|α = O(n), it implies
max1≤i≤n|ani|α ≤ Cn. Therefore, we have
n∑
i=1
|ani|k =
n∑
i=1
|ani|α|ani|k−α ≤ Cnn(k−α)/α = Cnk/α (2.14)
Journal of Inequalities and Applications 7
for all k ≥ α. Hence,∑ni=1 |ani|2 = O(n2/α). Taking q > γ , we have
I32 ≤ C
∞∑
n=1
n−1b−qn
[
n∑
i=1
|ani|2
]q/2
≤ C
∞∑
n=1
n−1b−qn n
q/α = C
∞∑
n=1
n−1
(
logn
)−q/γ
< ∞.
(2.15)
Proof of Theorem 1.4. By (1.9), a standard computation (see page 120 of Baum and Katz [11]
or page 1472 of An and Yuan [9]), and the Borel-Cantelli Lemma, we have
max1≤j≤2i
∣∣∣∑ji=1 aniXi
∣∣∣
2(i+1)/α
(
log 2i+1
)1/γ −→ 0 a.s. (i −→ ∞). (2.16)
For any n ≥ 1, there exists an integer i such that 2i−1 ≤ n < 2i. So
max
2i−1≤n<2i
∣∣∣∑nj=1 anjXj
∣∣∣
bn
≤
max1≤j≤2i
∣∣∣∑ji=1 anjXj
∣∣∣
2(i−1)/α
(
log 2i−1
)1/γ = 2
2/α
max1≤j≤2i
∣∣∣∑nj=1 anjXj
∣∣∣
2(i+1)/α
(
log 2i+1
)1/γ
(
i + 1
i − 1
)1/γ
.
(2.17)
From (2.16) and (2.17), we have
lim
n→∞
b−1n
n∑
i=1
aniXi = 0 a.s. (2.18)
Acknowledgments
The authors thank the Academic Editor and the reviewers for comments that greatly
improved the paper. This work is partially supported by Anhui Provincial Natural Science
Foundation (no. 11040606M04), Major Programs Foundation of Ministry of Education
of China (no. 309017), National Important Special Project on Science and Technology
(2008ZX10005-013), and National Natural Science Foundation of China (11001052, 10971097,
and 10871001).
References
[1] R. C. Bradley, “On the spectral density and asymptotic normality of weakly dependent random
fields,” Journal of Theoretical Probability, vol. 5, no. 2, pp. 355–373, 1992.
[2] W. Bryc and W. Smoleński, “Moment conditions for almost sure convergence of weakly correlated
random variables,” Proceedings of the American Mathematical Society, vol. 119, no. 2, pp. 629–635, 1993.
[3] S. C. Yang, “Some moment inequalities for partial sums of random variables and their application,”
Chinese Science Bulletin, vol. 43, no. 17, pp. 1823–1828, 1998.
[4] Q. Y. Wu, “Convergence for weighted sums of ρ̃ mixing random sequences,” Mathematica Applicata,
vol. 15, no. 1, pp. 1–4, 2002 (Chinese).
8 Journal of Inequalities and Applications
[5] Q. Wu and Y. Jiang, “Some strong limit theorems for ρ̃-mixing sequences of random variables,”
Statistics & Probability Letters, vol. 78, no. 8, pp. 1017–1023, 2008.
[6] M. Peligrad and A. Gut, “Almost-sure results for a class of dependent random variables,” Journal of
Theoretical Probability, vol. 12, no. 1, pp. 87–104, 1999.
[7] S. X. Gan, “Almost sure convergence for ρ̃-mixing random variable sequences,” Statistics & Probability
Letters, vol. 67, no. 4, pp. 289–298, 2004.
[8] S. Utev and M. Peligrad, “Maximal inequalities and an invariance principle for a class of weakly
dependent random variables,” Journal of Theoretical Probability, vol. 16, no. 1, pp. 101–115, 2003.
[9] J. An and D. M. Yuan, “Complete convergence of weighted sums for ρ∗-mixing sequence of random
variables,” Statistics & Probability Letters, vol. 78, no. 12, pp. 1466–1472, 2008.
[10] K. Budsaba, P. Chen, and A. Volodin, “Limiting behaviour of moving average processes based
on a sequence of ρ− mixing and negatively associated random variables,” Lobachevskii Journal of
Mathematics, vol. 26, pp. 17–25, 2007.
[11] L E. Baum and M. Katz, “Convergence rates in the law of large numbers,” Transactions of the American
Mathematical Society, vol. 120, pp. 108–123, 1965.
[12] P. L. Hsu and H. Robbins, “Complete convergence and the law of large numbers,” Proceedings of the
National Academy of Sciences of the United States of America, vol. 33, pp. 25–31, 1947.
[13] P. Erdös, “On a theorem of Hsu and Robbins,” Annals of Mathematical Statistics, vol. 20, pp. 286–291,
1949.
[14] F. Spitzer, “A combinatorial lemma and its application to probability theory,” Transactions of the
American Mathematical Society, vol. 82, pp. 323–339, 1956.
[15] S. H. Sung, “On the strong convergence for weighted sumsof random variables,” Statistical Papers. In
press.
[16] P. Chen, T.-C. Hu, X. Liu, and A. Volodin, “On complete convergence for arrays of rowwise negatively
associated random variables,” Rossiı̆skaya Akademiya Nauk, vol. 52, no. 2, pp. 393–397, 2007.


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