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Estimation of Parameters in a Bivariate Generalized Exponential Distribution Based on Type-II Censored Samples
- Issue Date
- Jan-2016
- Publisher
- Dekker
- Keywords
- Bayesian estimation; Dependence measure; Maximum likelihood estimation; Monte Carlo simulation; Numerical method
- Citation
- Communications in Statistics Part B: Simulation and Computation, v.45, no.10, pp 3776 - 3797
- Pages
- 22
- Indexed
- SCIE
SCOPUS
- Journal Title
- Communications in Statistics Part B: Simulation and Computation
- Volume
- 45
- Number
- 10
- Start Page
- 3776
- End Page
- 3797
- ISSN
- 0361-0918
1532-4141
- Abstract
- 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.
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