Open AccessBayesian Inference of Proportional Hazard Rate Model under Progressively Type-II Censored Sample
Author(s) -
MingHsiao Hu,
Haiping Ren
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/740/1/012039
Subject(s) - estimator , bayesian probability , bayesian inference , shrinkage estimator , statistics , mathematics , bayesian average , monte carlo method , inference , bayes estimator , econometrics , bayesian linear regression , computer science , bias of an estimator , artificial intelligence , minimum variance unbiased estimator
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.