Limit theorems for delayed sums of random sequence
© Fang-qing and Zhong-zhi; licensee Springer. 2012
Received: 29 October 2011
Accepted: 31 May 2012
Published: 31 May 2012
For a sequence of arbitrarily dependent random variables (X n )n∈Nand Borel sets (B n )n∈N, on real line the strong limit theorems, represented by inequalities, i.e. the strong deviation theorems of the delayed average are investigated by using the notion of asymptotic delayed log-likelihood ratio. The results obtained popularizes the methods proposed by Liu.
Mathematics Subject Classification 2000: Primary, 60F15.
are called the (forward) delayed first arithmetic means (See ). In , using the limiting behavior of delayed average, Chow found necessary and sufficient conditions for the Borel summability of i.i.d. random variables and also obtained very simple proofs of a number of well-known results such as the Hsu-Robbins-Spitzer-Katz theorem. In , Lai studied the analogues of the law of the iterated logarithim for delayed sums of independent random variables. Recently, Chen  has presented an accurate description the limiting behavior of delayed sums under a non-identically distribution setup, and has deduced Chover-type laws of the iterated logarithm for them.
Our aim in this article is to establish strong deviation theorems (limit theorem expressed by inequalities, see ) of delayed average for the dependent absolutely continuous random variables. By using the notion of asymptotic delayed log-likelihood ratio, we extend the analytic technique proposed by Liu  to the case of delayed sums. The crucial part of the proof is to construct a delayed likelihood ratio depending on a parameter, and then applies the Borel-Cantelli lemma.
Throughout, let (X n )n∈Nbe a sequence of absolutely continuous random variables on a fixed probability space with the joint density function g1, n(x1,..., x n ), n ∈ N, and f j (x), j = 1, 2,... be the the marginal density function of random variable X j . (k n )n∈Nbe a subsequence of positive integers, such that, for every ε > 0, .
is called asymptotic delayed log-likelihood ratio, where denotes the joint density function of random vector , ω is a sample point (with log 0 = -∞).
It will be shown in Lemma 1 that a.e. in any case.
Remark 1. It will be seen below that has the analogous properties of the likelihood ratio in , Although is not a proper metric among probability measures, we nevertheless consider it as a measure of "discrimination" between the dependence (their joint distribution) and independence (the product of their marginals). Obviously, , a.e. n∈ N if (X n )n∈Nis independent. In view of the above discussion of the asymptotic logarithmic delayed likelihood ratio, it is natural for us to think of as a measure how far (the random deviation) of (X n )n∈Nis from being independent and how dependent they are. The closer approaches to 0, the smaller the deviation is.
for any ε > 0, (1.3) follows immediately. □
2. Main results and proofs
wherebe the indicator function of B n .
If , (2.16) holds trivially. Since P (A* ∩ A(1)) = 1, (2.2) holds by (2.16), when c > 0.
since P (A(e)) = 1, (2.2) also holds by (2.17) when c = 0. □
If , (2.27) holds trivially. Since P (A* ∩ A(1)) = 1, (2.19) holds by (2.27), when c' > 0. (2.19) also holds trivially when c' = 0. □
Remark 2. In case , a.e., we cannot get a better lower bound of . This motivates the following problem: under the conditions of Theorem 2, how to get a better lower bound of in case of , a.e.?
F m, n = the observed frequency of values that are ≤ x from time m to m + n - 1. The F1,nis the usual empirical distribution function, hence the name given above.
In particular, let B = (-∞, x], x ∈ R in Theorems 1 and 2, we can get a strong limit theorem for the generalized empirical distribution function.
Similarly, we have , a.e. hence (2.28) follows immediately. □
Remark 3. Let B n = B, Corollary 2 implies that which gives the strong law of large numbers for the delayed arithmatic means.
This work is supported by The National Natural Science Foundation of China (Grant No. 11071104) and the An Hui University of Technology research grant: D2011025. The authors would like to thank two referees for their insightful comments which resulted in improving Theorems 1, 2 and Corollary 2 significantly.
- Zygmund A: Trigonometric Series 1. Cambridge Universitiy Press, Cambridge; 1959.Google Scholar
- Chow YS: Delayed sums and Borel summability for independent, identically distributed random variables. Bull Inst Math Academia Sinica 1972, 1: 207–220.Google Scholar
- Lai TL: Limit theorems for delayed sums. Ann Probab 1974, 2(3):432–440. 10.1214/aop/1176996658View ArticleGoogle Scholar
- Chen PY: Limiting behavior of delayed sums under a non-identically distribution setup. Ann Braz Acad Sci 2008, 80(4):617–625.View ArticleGoogle Scholar
- Liu W: Strong deviation theorems and analytical method. Academic press, Beijing; 2003.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.