Point and interval estimation based on joint progressive censoring data from two Rayleigh-Weibull distribution with applications

Abstract

The exploration of progressively censored data has garnered considerable attention in recent years. This research delves into the joint progressive censoring scheme applied to two populations. It presupposes that the lifespan distribution of items from these populations follows Rayleigh-Weibull distributions, characterized by varying shape and scale parameters. Within the framework of the joint progressive censoring scheme, we investigate maximum likelihood estimators for unknown parameters wherever applicable. Bayesian inferences for these parameters are presented using a Gamma prior. It’s worth noting that deriving Bayes estimators and their associated credible intervals is not feasible, hence we suggest employing the importance sampling technique for computation. To illustrate the methodologies, we analyze real-life data for demonstrative purposes, and Monte Carlo simulations are carried out to compare the performances of all the proposed methods.