A Note on Almost Sure Central Limit Theorem in the Joint Version for the Maxima and Sums
© Qing-pei Zang et al. 2010
Received: 30 March 2010
Accepted: 25 May 2010
Published: 20 June 2010
Let be a sequence of independent and identically distributed (i.i.d.) random variables and denote , . In this paper, we investigate the almost sure central limit theorem in the joint version for the maxima and sums. If for some numerical sequences , we have for a nondegenerate distribution , and is a bounded Lipschitz 1 function, then almost surely, where stands for the standard normal distribution function, ,and , .
1. Introduction and Main Results
For Gaussian sequences, Csáki and Gonchigdanzan  investigated the validity of (1.2) maxima of stationary Gaussian sequences under some mild condition. Furthermore, Chen and Lin  extended it to nonstationary Gaussian sequences. As for some other dependent random variables, Peligrad and Shao  and Dudziński  derived some corresponding results about ASCLT. The almost sure central limit theorem in the joint version for log average in the case of independent and identically distributed random variables is obtained by Peng et al. ; a joint version of almost sure limit theorem for log average of maxima and partial sums in the case of stationary Gaussian random variables is derived by Dudziński .
All the above results are related to the almost sure logarithmic version; in this paper; inspired by the results of Berkes and Csáki , we further study ASCLT in the joint version for the maxima and partial sums with another weighted sequence ( ). Now, we state our main result as follows.
Then, we can derive a corresponding result in Theorem 1.1.
2. Proof of Our Main Result
Proof of Theorem 1.1.
almost surely. Since , the convergence of the subsequence implies that the whole sequence converges almost surely. Hence the proof of (1.5) is completed for . Via the same arguments, we can obtain (1.5) for .
The authors thank two anonymous referees for their useful comments. They would like to thank professor Zhengyan Lin of Zhejiang University in China for his help. The work has been supported by the Young Excellent Talent Foundation of Huaiyin Normal University.
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