| 摘要: |
| 概率极限理论是概率论的主要分支之一，是概率统计学科中非常重要的理论基础；经典的极限理论是以独立随机变量为主要研究对象，但实际大部分随机变量是非独立的，负相关随机变量序列就是相依随机变量序列中的一类典型且应用广泛的随机变量序列，针对负相关随机变量加权和序列的极限问题，应用负相关序列、截尾和矩不等式等知识，推广了负相关随机变量加权和的矩完全收敛性，并给出了一个NA随机变量完全矩收敛的线性过程，所得结果改进了的相应结果。 |
| 关键词: 矩完全收敛性 加权和 负相关随机变量 线性过程 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Complete Moment Convergence for Weighted Sums of Negatively Associated Random Variables |
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DAI Ze xing，LU Wen hua
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| Abstract: |
| Probability limit theory is one of the main branches of probability theory and is a very important theoretical basis in probability statistics The classical limit theory is based on independent random variables as the main research object，but in practice，most of the random variables are not independent，and the negatively related random variable sequence is a typical and widely used random variable sequence in the sequence of dependent random variables For the limit problem of negatively correlated random variable weights and sequences，in this paper，we study the complete moment convergence for weighted sums of arrays of negatively associated(NA，for short) random variables As an application，we present the complete moment convergence of linear processes based on NA random variables The results obtained in this generalized paper improve the corresponding theorems of Baek et al |
| Key words: complete moment convergence weighted sums negatively associated random variable linear processes |
