Stress–Strength Parameter Estimation Based on Type-II Progressive Censored Samples for a Weibull-Half-Logistic Distribution

Abstract

To produce more flexible model, the Bayesian and classical inference of the stress–strength parameter, R, is studied under Type-II progressive censored samples, when stress and strength are two independent Weibull-half-logistic variables. In classical inference, the maximum likelihood estimation, approximation maximum likelihood estimation, uniformly minimum variance unbiased estimate and asymptotic confidence intervals of R are considered. Moreover, in Bayesian inference, two approximation Bayes estimates, exact Bayes estimate and highest posterior density intervals of R, are derived. These estimations are considered in different cases. Furthermore, the Monte Carlo simulations are applied to compare the performance of different methods. Two data sets are analyzed for illustrative aims.