MULTICOMPONENT STRESS-STRENGTH RELIABILITY ESTIMATION OF INVERSE PARETO LIFETIME MODEL UNDER PROGRESSIVELY CENSORED DATA

This article deals with the problem of estimation of the multicomponent stress-strength (MSS) reliability from the inverse Pareto lifetime model under progressively censored data. The MSS reliability is estimated in the case when both the stress and strength variables have inverse Pareto lifetime models with different parameters. To estimate the MSS reliability, the maximum likelihood, asymptotic confidence interval, Bayesian and highest posterior density credible interval estimation methods are used. The Bayes estimate of MSS reliability is calculated using the Metropolis-Hastings algorithm under the linear exponential loss function. A simulation study is conducted to check the performance of the different estimates proposed in the article. To illustrate the proposed methods a real-life example is analyzed.