EXPONENTIATED PARETO DISTRIBUTION: A BAYES STUDY UTILIZING MCMC TECHNIQUE UNDER UNIFIED HYBRID CENSORING SCHEME

Received 4/3/2018 Revised 25/3/2018 Accepted 6/7/2018 Abstract: This article aims to study the problem of point and interval estimations of the exponentiated Pareto distribution utilizing unified hybrid censored scheme (HCS). We utilize three methods, including the maximum likelihood, parametric bootstrap and Bayes of estimating the unknown parameters, reliability, hazard rate functions and coefficient of variation. Furthermore, Markov Chain Monte Carlo samples utilizing importance sampling scheme are utilized to generate the Bayes estimates and the credible intervals for unknown quantities. The findings of Bayes method computed using balanced loss function. The suggested methods can be understood by analysing a set of real data.