Bayesian Inference on Type-Ⅰ Progressively Hybrid Competing Risks Model  

作　　者：ZHANG Chun-fang Sill Yi-min WU Min 

机构地区：[1]School of Mathematics and Statistics, Xidian University, Xi'an, 710126, China [2]School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi'an, 710072, China [3]School of Economics Management, Shanghai Maritime University, Shanghai, 201306, China

出　　处：《Chinese Quarterly Journal of Mathematics》2018年第2期122-131,共10页数学季刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China（71571144,71401134,71171164,11701406）; Supported by the International Cooperation and Exchanges in Science and Technology Program of Shaanxi Province（2016KW-033）

摘　　要：In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.

关 键 词：Competing risks Hierarchical Bayesian inference Progressively hybrid censoring Metropolis-Hastings algorithm 

分 类 号：O212.8[理学—概率论与数理统计]