Abstract
In this note, we derive, under some natural assumptions, a general pattern of the almost sure central limit theorem in the joint version for the maxima and sums.
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1 Introduction and main results
Let be a sequence of independent and identically distributed (i.i.d.) random variables and , for . If , , then the classical almost sure central limit theorem (ASCLT) has the simplest form as follows:
where, here and in the sequel, is the indicator function of the event A and stands for the standard normal distribution function. This result was firstly proved independently by Brosamler [1] and Schatte [2] under a stronger moment condition. Since then, this type of almost sure theorem, which mainly dealt with logarithmic average limit theorems, has been extended in various directions. In particular, Fahrner and Stadtmüller [3] and Cheng et al. [4] extended the almost sure convergence for partial sums to the case of maxima of i.i.d. random variables. Namely, under some natural conditions, they proved as follows:
where denotes the set of continuity points of G, and where and satisfy
with being one of the extreme value distributions, i.e.,
for some , or
for some . These three distributions are often called the Gumbel, the Frechet and the Weibull distributions, respectively.
For Gaussian sequences, Csáki and Gonchigdanzan [5] investigated, under some mild conditions, the validity of (1.2) for maxima of stationary Gaussian sequences. Furthermore, Chen and Lin [6] extended it to non-stationary Gaussian sequences. As for some other dependent random variables, Peligrad and Shao [7] and Dudziński [8] derived some corresponding results about ASCLT. In addition, the almost sure central limit theorem in the joint version for log-average of maxima and partial sums of independent and identically distributed random variables was obtained by Peng et al. [9], whereas the joint version of the almost sure limit theorem for log-average of maxima and partial sums of stationary Gaussian random variables was derived by Dudziński [10].
In statistical context, we are very concerned with the ASCLT in the joint version for the maxima and partial sums. The goal of this note is to investigate the general pattern of the ASCLT for the maxima and partial sums of i.i.d. random variables by the method provided by Hörmann [11]. He showed the following result.
Theorem A Let be independent random variables with partial sums . Assume that for some numerical sequences and , we have
with some (possibly degenerate) distribution function H.
Suppose, moreover, that
and
for some positive constants C, β.
Assume finally that
and that
Then, if f is a bounded Lipschitz function on the real line or the indicator function of a Borel set with , we have
Now, we may state our main result as follows.
Theorem 1.1 Let be a sequence of independent and identically distributed (i.i.d.) random variables with non-degenerate and continuous common distribution function F satisfying and . Suppose that, for a non-degenerate distribution G, there exist some numerical sequences , such that
Suppose, moreover, that the positive weights , , satisfy the following conditions:
() ;
() is nonincreasing for some ;
() for some , where .
Assume, in addition, that is a bounded Lipschitz function. Then
Remark 1.2 Since a set of bounded Lipschitz functions is tight in a set of bounded continuous functions, Theorem 1.1 is true for all bounded continuous functions .
Remark 1.3 It can be seen by routine approximation arguments similar, e.g., to those in Lacey and Philipp [12] that, under the conditions of Theorem 1.1, the result in (1.4) holds for indicator functions, i.e.,
Remark 1.4 The result of Berkes and Csáki [13] shows that the a.s. central limit theorem remains valid even with the sequence of weights
which at least includes a ‘halfway’ from logarithmic to ordinary averaging. Moreover, Hörmann [11] shows that this sequence obeys the a.s. central limit theorem for all . Due to the similar conditions on the sequence of weights, our result also holds for this sequence provided .
2 Proof of our main result
The following notations will be used throughout this section: , , , and for . Furthermore, and stand for and , respectively, and is the standard normal distribution function. The proof of our main result is based on the following lemmas.
Lemma 1 Under the assumptions of Theorem 1.1, we have
Proof It is easy to see that
For , we have, by the independence of , that
Now, we are in a position to estimate . From the fact that f is bounded and Lipschitzian, it follows that
where we used the fact that
for any nondecreasing function ψ on . In order to verify the last relation, let , and let U be a random variable uniformly distributed on . Then for all and the random variable has distribution F. Thus, the left-hand side of the above inequality equals
as claimed.
By the fact that f is Lipschitzian and due to the Cauchy-Schwarz inequality, we have
Thus, using (2.1)-(2.3), we get the desired result. □
Let f and be such as in the statement of Theorem 1.1 and
We will also prove the following auxiliary result.
Lemma 2 Let p be a positive integer. Then for , we have
Proof Without loss of generality, we may assume that . Thus, we have
Furthermore, we obtain that
The relations in (2.4), (2.5) imply the claim in Lemma 2. □
The following lemma will also be used.
Lemma 3 Let p be a positive integer. Then for , we have
Proof We can write
Thus, using the Hölder inequality, the Cauchy-Schwarz inequality and Lemma 2, we derive
This and the relation
imply the desired result. □
We will also prove the following lemma.
Lemma 4 For every , we have
Proof This lemma can be obtained from Lemmas 1 and 3 by making slight changes in the proof of Lemma 4 of Hörmann [11]. □
The following result will be needed in the proof of our main result.
Lemma 5 Suppose that , where ρ satisfies the assumption in () of Theorem 1.1. We have
Proof This result follows from Lemma 5 in Hörmann [11]. □
Proof of Theorem 1.1 Firstly, by Theorem 1.1. in Hsing [14] and our assumptions, we have
Then, in view of the dominated convergence theorem, we have
Hence, in order to complete the proof, it is sufficient to show
This follows from Lemmas 4 and 5 by applying similar arguments to those used in Hörmann [11].
In fact, from Lemmas 4 and 5 and the Markov inequality, we derive
By (), we have . Thus, we can choose an increasing subsequence such that . Then, choosing and using Borel-Cantelli lemma, we derive
For , we have
Since , the convergence of the subsequence implies that the whole sequence converges almost surely. This completes the proof of Theorem 1.1. □
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Acknowledgements
The author would like to thank the Editor and the two referees for careful reading of the paper and for their comments which have led to the improvement of this work. This work was supported by the Natural Science Research Project of Ordinary Universities in Jiangsu Province, P.R. China. Grant No. 12KJB110003.
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Zang, Qp. A note on the almost sure central limit theorems for the maxima and sums. J Inequal Appl 2012, 223 (2012). https://doi.org/10.1186/1029-242X-2012-223
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DOI: https://doi.org/10.1186/1029-242X-2012-223