Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type‐II Censored Data

Abstract

The rising demand for reliable products made comparative studies a must for the products manufactured from two different lines. For the comparative studies, researchers use the joint progressive type-II censoring scheme in order to minimize the capital and time involved in the experiment. In this article, we draw some inferences for inverse Rayleigh distribution based on joint progressive type-II censoring. The maximum likelihood estimation and the corresponding asymptotic confidence interval estimation are used as the classical estimation methods. The Bayes estimates are calculated under the squared error loss function (SELF) using Tierney-Kadane's approximation and Metropolis-Hastings algorithm along with the Bayes estimates highest posterior density credible intervals are also constructed. A Markov Chain Monte Carlo simulation study is carried out to compare different estimation methods. A real life problem is discussed for illustrative purposes.