Reliability estimation of a stress-strength model with non-identical component strengths under generalized progressive hybrid censoring scheme

Generalized progressive hybrid censoring schemes have become quite popular depending progressive hybrid censoring scheme cannot be applied when few failures occur before pre-determined time T. Otherwise, due to the necessity of more realistic stress strength models, this article considers to estimate the reliability of an s-out-of-k system with non-identical component strengths when component strengths and stress follow Weibull distributions under generalized progressive hybrid censoring scheme. The maximum likelihood estimation of the reliability of such a system is obtained with corresponding asymptotic and bootstrap confidence intervals. Further, Bayesian estimations are derived by using the Lindley's approximation and Markov Chain Monte Carlo method with the Metropolis-Hasting algorithm. The corresponding highest posterior density confidence intervals of the Bayes estimates are obtained. In Bayesian estimations symmetric and asymmetric loss functions are evaluated. Comparisons for the performances of the estimators are made with simulation studies and a real data example.