Complete convergence and complete moment convergence for widely orthant-dependent random variables

被引：17
|
作者：
Ding, Yang [1 ]
Wu, Yi [1 ]
Ma, Songlin [2 ]
Tao, Xinran [1 ]
Wang, Xuejun [1 ]
机构：
[1] Anhui Univ, Sch Math Sci, Hefei 230601, Anhui, Peoples R China
[2] Chaohu Univ, Sch Appl Math, Chaohu, Peoples R China
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2017年 / 46卷 / 16期
基金：
中国国家自然科学基金;
关键词：
Complete convergence; complete moment convergence; widely orthant-dependent random variables; PRECISE LARGE DEVIATIONS; UNIFORM ASYMPTOTICS; WEIGHTED SUMS; LARGE NUMBERS; STRONG LAW; ARRAYS; CONSISTENCY; ESTIMATOR; MODEL;
D O I：
10.1080/03610926.2016.1177085
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, we first establish the complete convergence for weighted sums of widely orthant-dependent (WOD, in short) random variables by using the Rosenthal type maximal inequality. Based on the complete convergence, we further study the complete moment convergence for weighted sums of arrays of rowwise WOD random variables which is stochastically dominated by a random variable X. The results obtained in the paper generalize the corresponding ones for some dependent random variables.
引用
收藏
页码：8278 / 8294
页数：17
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