Bayesian Inference of Proportional Hazard Rate Model under Progressively Type-II Censored Sample

Abstract

Abstract Statistical inference of the parameter of the proportional hazard rate model is studied based on progressive type-II censored sample. Bayesian and empirical Bayesian estimators are first obtained under a scaled squared error loss function. Then we derived a class of Bayesian shrinkage estimators motivated by the idea of Thompson’s shrinkage algorithm based on obtained estimators. Finally, a practical example and Monte Carlo simulations illustrate that the proposed Bayesian shrinkage estimators are more robust comparing to the Bayesian and empirical Bayesian estimators.