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.