Inference and optimum life testing plans based on Type-II progressive hybrid censored generalized exponential data

Abstract

This article considers inference based on Type-II progressive hybrid censored data for a generalized exponential distribution. The maximum likelihood (ML) estimates, Bayes estimates and corresponding interval estimates of unknown model parameters are derived. Prediction estimates and prediction intervals of censored observations are obtained under one- and two-sample Bayesian framework. A Monte Carlo simulation study is undertaken to compare the proposed methods of estimation. A real data set is analyzed for illustration purpose. Finally, optimal life testing plans are obtained under cost constraints using two different optimality criteria. A computational algorithm is proposed to compute the optimal plans.

Keywords:

• Bayesian prediction
• Entropy
• Generalized exponential distribution
• Maximum likelihood estimate
• MH algorithm

Acknowledgments

The authors thank two anonymous reviewers and an associate editor for their critical comments and helpful suggestions which have resulted in an improvement over the earlier version of this article. The research of the corresponding author is partially supported by Consejo Nacional de Ciencia y Tecnología (CONACYT) grant number CB 2016–2018 No. 252996.