Reliability estimation in multicomponent stress-strength model for generalized inverted exponential distribution

The problem reliability estimation of multicomponent stress-strength system is studied in this paper, when the strength components and stress component follow generalized inverted exponential distributions under progressive first failure censored data. Point estimate of reliability in a multicomponent stress-strength model is derived by using maximum likelihood and Bayes methods. We construct the asymptotic confidence interval and highest posterior density credible interval. Two bootstrap confidence intervals are proposed. The performances of the different estimation algorithms are assessed by the Monte Carlo simulations. Carbon fiber strength data and data on water capacity of the Shasta reservoir have been analyzed for the purpose of illustration.