Bayesian and Likelihood Estimation in Two Inverse Pareto Populations Under Joint Progressive Censoring

In the era of growing technologies and demand for more reliable products, a comparative study of products from various manufacturing units has become essential. Due to the time-saving and cost-effectiveness properties, the joint progressive Type-II censoring scheme is beneficial for dealing with such types of comparative studies. This article deals with the estimation of two inverse Pareto populations using joint progressively Type-II censored data. The Bayesian and maximum likelihood estimation methods are considered to estimate the unknown parameters. The asymptotic confidence intervals of the unknown parameters are constructed. The Bayes estimates of the parameters are computed using the linear exponential (Linex) loss function, and the corresponding highest posterior density (HPD) credible intervals are developed. The Tierney–Kadane (T–K) and Markov chain Monte Carlo (MCMC) approximation methods are used to obtain the Bayes estimates. A Monte Carlo simulation study is performed to measure the efficiency of developed estimates. For illustration purposes, two pairs of different real data sets are studied. Also, some optimal criteria are discussed to find the optimum censoring scheme.