Bayesian estimation of the survival characteristics for Hjorth distribution under progressive type-II censoring

Abstract

In this paper, the Bayes estimation procedure for the parameters and survival characteristics (survival and hazard functions) of the Hjorth distribution has been proposed with progressively type-II censored data. The Bayes estimators are derived with gamma prior and evaluated under squared error loss function. It is known that the censored observations create the complexity in Bayes estimation procedures. Therefore, two approximation techniques, namely Tierney–Kadane approximation method and Markov Chain Monte Carlo method have been used to compute the approximate Bayes estimators. The proposed estimates are compared with the usual maximum likelihood estimators through Monte Carlo simulations. Lastly, a medical data set has been considered to show the applicability of the proposed model as well study in real life scenario.