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Estimation of Parameters in a Bivariate Generalized Exponential Distribution Based on Type-II Censored Samples
- Kim, Seong Wook;
- Ng, Hon Keung Tony;
- Jang, Hakjin
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7초록
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. © 2016, Copyright © Taylor & Francis Group, LLC.
키워드
Bayesian estimation; Dependence measure; Maximum likelihood estimation; Monte Carlo simulation; Numerical method; Algorithms; Bayesian networks; Intelligent systems; Iterative methods; Markov processes; Maximum likelihood; Maximum likelihood estimation; Monte Carlo methods; Numerical methods; Bayesian estimations; Dependence measures; Estimation of parameters; Estimation procedures; Generalized exponential distribution; Iterative algorithm; Markov chain Monte Carlo method; Simulation studies; Parameter estimation
- 제목
- Estimation of Parameters in a Bivariate Generalized Exponential Distribution Based on Type-II Censored Samples
- 저자
- Kim, Seong Wook; Ng, Hon Keung Tony; Jang, Hakjin
- 발행일
- 2016-01
- 유형
- Article
- 권
- 45
- 호
- 10
- 페이지
- 3776 ~ 3797