Estimation of Parameters in a Bivariate Generalized Exponential Distribution Based on Type-II Censored Samples

被引：6

作者：

Kim, Seong W. ^{[1
]}

Ng, Hon Keung Tony ^{[2
]}

Jang, Hakjin ^{[1
]}

机构：

[1] Hanyang Univ, Dept Appl Math, Seoul, South Korea

[2] Southern Methodist Univ, Dept Stat Sci, Dallas, TX 75275 USA

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2016年 / 45卷 / 10期

基金：

新加坡国家研究基金会;

关键词：

Bayesian estimation; Dependence measure; Maximum likelihood estimation; Monte Carlo simulation; Numerical method; 62F10; 62F15; 62H12; EM ALGORITHM; KENDALLS TAU;

D O I：

10.1080/03610918.2015.1130834

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 article, we discuss the maximum likelihood estimation and Bayesian estimation procedures for estimating the parameters in an absolute continuous bivariate generalized exponential distribution based on Type-II censored samples. A Markov chain Monte Carlo method is applied to compute the Bayes estimates. We also propose a method to obtain the initial estimates of the parameters for the required iterative algorithm. A simulation study is used to evaluate the performance of the proposed estimation procedures. Two real data examples are utilized to illustrate the methodology developed in this manuscript.

引用

页码：3776 / 3797

页数：22

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