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A Limit Theorem for the Moment of Self-Normalized Sums
Journal of Inequalities and Applications volume 2009, Article number: 957056 (2009)
Abstract
Let be a sequence of independent and identically distributed (i.i.d.) random variables and is in the domain of attraction of the normal law and . For , we prove the precise asymptotics in Davis law of large numbers for
1. Introduction and Main Result
Throughout this paper, we let be a sequence of random variables and is in the domain of attraction of the normal law and . Put
Also let Then by the well-known Davis laws of large numbers [1],
if and only if and .
Gut and Spătaru [2] proved its precise asymptotics as follows.
Theorem 1 A.
Suppose that and Then for ,
where stands for the absolute moment of the standard normal distribution.
It is well known that, for random variables, Chow [3] discussed the complete moment convergence, and got the following result.
Theorem 1 B.
Let be a sequence of random variables with . Assume , , and Then for any ,
On the other hand, the past decade has witnessed a significant development on the limit theorems for the so-called self-normalized sum . Bentkus and Götze [4] obtained Berry-Esseen inequalities for self-normalized sums. Wang and Jing [5] derived exponential nonuniform Berry-Esseen bound. Giné et al. [6], established asymptotic normality of self-normalized sums.
Theorem 1 C.
Let be a sequence of random variables with . Then for any ,
holds, if and only if is in the domain of attraction of the normal law, where is the distribution function of the standard normal random variable.
Shao [7] showed a self-normalization large deviation result for without any moment conditions.
Theorem 1 D.
Let be a sequence of positive numbers with and as . If and is slowly varying as then
Since then, many subsequent developments of self-normalized sums have been obtained. For example, Csörgő et al. [8] have established Darling-Erdös theorem for self-normalized sums, and they [9] have also obtained Donsker's theorem for self-normalized partial sums processes.
Inspired by the above results, in this note we study the precise asymptotics in Davis law of large numbers for the moment of self-normalized sums. Our main result is as follows.
Theorem 1.1.
Suppose is in the domain of attraction of the normal law and . Then, for and , one has
here and in the sequel, is the standard normal random variable.
Remark 1.2.
If and , by the strong law of large numbers, we have Then, we can easily obtain the following result:
Remark 1.3.
As is well known, the strong approximation method is taken in order to obtain such an analogous result, however, this method is not applicable here.
2. Proof of Theorem 1.1
In this section, we set for and . Here and in the sequel, will denote positive constants, possibly varying from place to place, and means the largest integer . The proof of Theorem 1.1 is based on the following propositions.
Proposition 2.1.
For , one has
Proof.
Via the change of variable , we have
Proposition 2.2.
For , one has
Proof.
Set Then, by (1.5), it is easy to see as Observe that
where
Thus for , it is easy to see
Now we are in a position to estimate . From (1.6), and by applying to it, we can obtain that for large enough and any , there exist C and b such that for . In particular, for , there exists such that
Hence, by Markov's inequality and (2.7), we have
For , by Markov's inequality and (2.7), we have
From Cauchy inequality, it follows that
Therefore
Denote , then, since the weighted average of a sequence that converges to 0 also converges to 0, it follows that, for any ,
The proof is completed.
Proposition 2.3.
For , one has
Proof.
Note that
So this proposition is proved now.
Proposition 2.4.
For , one has
Proof.
Note that
where
For , by (2.7), we have
For , using (2.7) again, we have
By noting that (2.10), it is easily seen that
Combining (2.18), (2.19), and (2.20), the proposition is proved.
Our main result follows from the propositions using the triangle inequality.
References
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Chow YS: On the rate of moment convergence of sample sums and extremes. Bulletin of the Institute of Mathematics. Academia Sinica 1988,16(3):177–201.
Bentkus V, Götze F: The Berry-Esseen bound for Student's statistic. The Annals of Probability 1996,24(1):491–503.
Wang Q, Jing B-Y: An exponential nonuniform Berry-Esseen bound for self-normalized sums. The Annals of Probability 1999,27(4):2068–2088. 10.1214/aop/1022677562
Giné E, Götze F, Mason DM: When is the student -statistic asymptotically standard normal? The Annals of Probability 1997,25(3):1514–1531.
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Acknowledgments
The author thanks the referees for pointing out some errors in a previous version, as well as for several comments that have led to improvements in this work. Thanks are also due to Doctor Ke-ang Fu of Zhejiang University in china for his valuable suggestion in the preparation of this paper.
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Zang, Qp. A Limit Theorem for the Moment of Self-Normalized Sums. J Inequal Appl 2009, 957056 (2009). https://doi.org/10.1155/2009/957056
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DOI: https://doi.org/10.1155/2009/957056