Inference on multicomponent stress-strength model using progressively censored data from exponentiated Pareto distribution

This article deals with the Bayesian and classical estimation approaches for the reliability of stress-strength of a multicomponent system assuming both the stress and strength variables follow exponentiated Pareto distribution independently based on the progressively censored data. In the classical estimation, the maximum likelihood estimate, asymptotic confidence and two bootstrap confidence intervals boot-t \amp boot-p are constructed for multicomponent stress-strength (MSS) reliability. In the Bayesian estimation, the Bayes estimates under the squared error loss function using Markov chain Monte Carlo (MCMC) techniques are obtained. The highest posterior density credible interval based on the MCMC method of the MSS reliability are constructed. The different estimates obtained are compared using a Monte Carlo simulation study which is carried out for various sample sizes and different censoring schemes. Two different real data sets are studied to illustrate the real-life applications of the study. Finally, the conclusions based on the study with the future scope of the work are provided.