Asymptotic Tail Probabilities of Sums of Dependent Subexponential Random Variables

被引：115

作者：

Geluk, Jaap ^{[2
]}

Tang, Qihe ^{[1
]}

机构：

[1] Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA

[2] Petr Inst, Dept Math, Abu Dhabi, U Arab Emirates

来源：

JOURNAL OF THEORETICAL PROBABILITY | 2009年 / 22卷 / 04期

关键词：

Asymptotic tail probability; Convolution; Dominated variation; Farlie-Gumbel-Morgenstern family; Subexponentiality; RANDOM-WALKS; DISTRIBUTIONS; INCREMENTS; BEHAVIOR;

D O I：

10.1007/s10959-008-0159-5

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 study the asymptotic behavior of the tail probabilities of sums of dependent and real-valued random variables whose distributions are assumed to be subexponential and not necessarily of dominated variation. We propose two general dependence assumptions under which the asymptotic behavior of the tail probabilities of the sums is the same as that in the independent case. In particular, the two dependence assumptions are satisfied by multivariate Farlie-Gumbel-Morgenstern distributions.

引用

页码：871 / 882

页数：12

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