Estimation of Power Lomax Distribution for Censored Data With Applications

In this study, power Lomax (PL) distribution parameters are estimated under an adaptive Type‐II progressive censoring scheme, utilizing both frequentist and Bayesian statistical estimations. The model parameters, reliability and hazard functions, and coefficient of variation are all determined using an iterative procedure in the frequentist estimation. Furthermore, the asymptotic normality features of maximum likelihood estimates (MLEs) are used to calculate MLEs and asymptotic confidence intervals. The Bayesian method estimates under both symmetric and asymmetric loss functions by using the Markov Chain Monte Carlo (MCMC) technique. The performance of the Bayesian estimates and the MLEs is compared and contrasted in a simulated study. Finally, a numerical analysis of a real data set is presented to illustrate the application of the proposed inferential processes.