Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications

This study presents a comparative analysis of frequentist and Bayesian estimation techniques for the parameters of the inverse power Lomax distribution, employing an adaptive Type‐II progressive censoring approach. The maximum likelihood estimations (MLEs) and their corresponding asymptotic confidence intervals are derived. Bayesian estimation is carried out via the Markov chain Monte Carlo (MCMC) method, considering both symmetric and asymmetric loss functions. A simulation study is used to assess and compare the performance of the Bayes estimates and MLEs. The study also investigates Bayesian prediction of order statistics. The proposed inferential procedures are then validated using a numerical study based on a real dataset.