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山东大学学报(理学版) ›› 2016, Vol. 51 ›› Issue (6): 24-29.doi: 10.6040/j.issn.1671-9352.0.2015.386

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随机变量阵列的几乎处处中心极限定理

刘洋1,冯志伟1,陈平炎2*   

  1. 1.暨南大学统计学系, 广东 广州 510630;2. 暨南大学数学系, 广东 广州 510630
  • 收稿日期:2015-08-10 出版日期:2016-06-20 发布日期:2016-06-15
  • 通讯作者: 陈平炎(1968— ),男,教授,研究方向为概率极限理论、应用概率、分形随机场.E-mail:tchenpy@jnu.edu.cn E-mail:liuy199103@163.com
  • 作者简介:刘洋(1991— ),男,硕士研究生,研究方向为极限理论及分析概率.E-mail:liuy199103@163.com
  • 基金资助:
    国家自然科学基金资助项目(11271161)

Almost sure central limit theorem for arrays of random variables

LIU Yang1, FENG Zhi-wei1, CHEN Ping-yan2*   

  1. 1.Department of Statistics, Jinan University, Guangzhou 510630, Guangdong, China;
    2. Department of Mathematics, Jinan University, Guangzhou 510630, Guangdong, China
  • Received:2015-08-10 Online:2016-06-20 Published:2016-06-15

摘要: 随机变量序列与阵列,关于其几乎处处中心极限定理存在比较大的差异,并且对其权重系数的选取有一定要求。对随机变量阵列两种不同权重选择条件进行研究,得到一些关于随机变量阵列的几乎处处中心极限定理及推论。

关键词: 随机变量阵列, 权重, 几乎处处中心极限定理

Abstract: It is different between sequence and array of random variables on the almost sure central limit theorem, and the selection of its weight coefficient has certain requirements. In this paper, we study two kinds of different weight selection conditions for arrays of random variables, and obtain the almost sure central limit theorems and inferences for arrays of random variables.

Key words: weight, array of random variables, almost sure central limit theorem

中图分类号: 

  • O211.4
[1] SCHATTE P. On strong versions of the central limit theorem[J]. Statistics and Probability Letters, 1988, 137:249-256.
[2] LACEY M T, Philipp W. A note on the almost sure central limit theorem[J]. Statistics and Probability Letters, 1990, 9:201-205.
[3] BERKES I. On the almost sure central limit theorem and domains of attraction[J]. Probab Theory Related Fields, 1995, 102:1-18.
[4] BERKES I, Dehling H. Some limit theorems in log density[J]. Ann Probab, 1993, 21:1640-1670.
[5] BERKES I, Dehling H. On the almost sure central limit theorem for random variables with inJnite variance[J]. Theoret Probab, 1994, 7:667-680.
[6] CSORGO M, HORVATH L. Invariance principles for logarithmic averages[J]. Math Proc Cambridge Phil Soc, 1992, 112:195-205.
[7] PELIGRAD M, SHAO Q M. A note on the almost sure central limit theorem for weakly dependent random variables[J]. Statistics and Probability Letters, 1995, 22:131-136.
[8] DUDZINSKI M. An almost sure limit theorem for the maxima and sums of stationary Gaussian sequences[J]. Statistics and Probability Letters, 2003, 23:139-152.
[9] PELIGRAD M, REVESZ P. On the almost sure central limit theorem[M]. Almost everywhere convergence II. Boston, MA: Academic Press, 1991.
[10] BERKES I, CSAKI E. A universal result in almost sure central limit theory[J]. Stochastic Process and Applications, 2001, 94:105-134.
[11] CHEN P, YE X, HU T C. A strong law and a law of the single logarithm for arrays of rowwise independent random variables[J]. Statistics and Probability Letters, 2016, 110:169-174.
[12] 林正炎,陆传荣,苏中根.概率极限理论基础[M]. 北京:高等教育出版社,1999:85-95.
[13] 汪嘉冈.现代概率论基础[M].上海: 复旦大学出版社, 2005:98-99.
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