Abstract
- This article explores and derives the estimation of the multicomponent stress–strength (MSS) reliability parameter, assuming thatthe samples are coming from the inverted exponentiated Pareto distribution using a progressively first-failure censored scheme.To estimate the MSS reliability, both classical and Bayesian approaches are adopted. In the classical approach, the maximum likelihood and the asymptotic confidence interval estimation methods are used. The Bayes estimates with their corresponding highest posterior density credible interval estimates are obtained under the Bayesian approach, under the linear exponential loss function under both the non-informative and gamma informative priors. In addition, to compute the Bayes estimates, Markov chain Monte Carlo methods are used. To compare the efficacy of the different estimation strategies adopted in this paper, aMonte Carlo simulation study is carried out. To demonstrate the applicability of the proposed methodology, two real-life scenariosresulting/arising from two different carbon fiber data sets are re-analyzed.