Inference of reliability in a multicomponent stress–strength model under generalized progressive hybrid censoring

Abstract

This paper considers the reliability inference of a multicomponent stress–strength model by assuming the dependent Weibull stress and strength variables based on Gumbel copula under generalized progressive hybrid censoring (GPHC). The point estimates of unknown parameters and reliability are obtained by using frequentist and Bayesian approaches, and their interval estimates are constructed utilizing the asymptotic normality property of maximum likelihood estimates (MLE) and Markov Chain Monte Carlo (MCMC) samples. Extensive simulations are performed to compare the proposed methods. Finally, one real data set is analyzed to demonstrate the applicability of our study.