On normal approximation for strongly mixing random variables

被引：8
|
作者：
Sunklodas, J. [1 ]
机构：
[1] Inst Math & Informat, LT-08663 Vilnius, Lithuania
来源：
ACTA APPLICANDAE MATHEMATICAE | 2007年 / 97卷 / 1-3期
关键词：
normal approximation; bounded Lipschitz metric; strong mixing condition; weakly dependent random variables; Stein's method;
D O I：
10.1007/s10440-007-9122-1
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we estimate the difference | Eh(Z(n))- Eh(N)|, where Z(n) is the sum of n centered and normalized random variables (without the stationarity assumption) satisfying the strong mixing condition, N is a standard normal random variable, and h : R -> R is a Lipschitz function. In particular cases, the obtained upper bounds are of order O(n(-1/2)).
引用
收藏
页码：251 / 260
页数：10
相关论文
共 50 条
12345