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

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.

Keywords:

• Bayesian estimation
• Dependence measure
• Maximum likelihood estimation
• Monte Carlo simulation
• Numerical method.

Mathematics Subject Classification:

• 62F10
• 62F15
• 62H12.

Appendix: Observed Fisher Information Matrices

Acknowledgments

The authors are grateful to the associate editor and two anonymous reviewers for their constructive comments that led to this substantial improvement on an earlier version of the article. The authors would like to thank Yaxing Li for generating some of the simulation results presented in this manuscript.

Funding

S. W. Kim's work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0028933). H. K. T. Ng's work was supported by a grant from the Simons Foundation (#280601 to Tony Ng).