BAYESIAN AND CLASSICAL INFERENCE FOR TOPP-LEONE INVERSE WEIBULL DISTRIBUTION BASED ON TYPE-II CENSORED DATA

BAYESIAN AND CLASSICAL INFERENCE FOR TOPP-LEONE INVERSE WEIBULL DISTRIBUTION BASED ON TYPE-II CENSORED DATA

This paper delves into an examination of both non-Bayesian and Bayesian estimation techniques for determining the Topp-leone inverse Weibull distribution parameters based on progressive Type-II censoring. The first approach employs expectation maximization (EM) algorithms to derive maximum likelihood estimates for these variables. Subsequently, Bayesian estimators are obtained by utilizing symmetric and asymmetric loss functions such as Squared error and Linex loss functions. The Markov chain Monte Carlo method is invoked to obtain these Bayesian estimates, solidifying their reliability in this framework.