Estimation of generalized exponential distribution based on an adaptive progressively type-II censored sample

ABSTRACT

In this paper, based on an adaptive Type-II progressively censored sample from the generalized exponential distribution, the maximum likelihood and Bayesian estimators are derived for the unknown parameters as well as the reliability and hazard functions. Also, the approximate confidence intervals of the unknown parameters, and the reliability and hazard functions are calculated. Markov chain Monte Carlo method is applied to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. Moreover, results from simulation studies assessing the performance of our proposed method are included. Finally, an illustrative example using real data set is presented for illustrating all the inferential procedures developed here.

KEYWORDS:

• Adaptive type-II progressive censoring scheme
• Bayesian estimation
• generalized exponential distribution
• maximum likelihood estimation
• Markov chain Monte Carlo technique

MATHEMATICS SUBJECT CLASSIFICATION 2010:

• Primary: 62G30
• Secondary: 62F15

Acknowledgments

The authors express their sincere thanks to the referees for their constructive comments and suggestions on the original version of this manuscript, which led to this improved version. The third author thank the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia, for funding this work.

Disclosure statement

No potential conflict of interest was reported by the authors.