On estimation of P(V < U) for inverse Pareto distribution under progressively censored data

This article deals with the estimation of stress-strength reliability parameter R = ( V < U) for inverse Pareto distribution (IPD) based on progressively censored data, where V and U both are independent random variables representing stress and strength, respectively, following IPD. The maximum likelihood estimator and asymptotic confidence interval with its coverage probability for R are obtained. The Bayes estimator of R is computed under generalized entropy loss function based on non-informative and gamma informative priors using importance sampling technique. Also, the HPD credible interval with its coverage probability of R is constructed. The performance of different estimators and various censoring schemes are compared numerically using a Monte Carlo simulation study. Finally, two different pairs of real data sets are analyzed for applicability of considered model and different estimation procedures.