Constant hazard function models with a change point: A Bayesian analysis using Markov chain Monte Carlo methods

作     者：Achcar, JA Loibel, S 

作者机构：Univ Sao Paulo Inst Ciencias Math BR-13560970 Sao Carlos SP Brazil 

出 版 物：《BIOMETRICAL JOURNAL》 (生物统计杂志)

年 卷 期：1998年第40卷第5期

页      面：543-555页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：constant hazard change-point Gibbs sampling Metropolis algorithm 

摘      要：Metropolis algorithms along with Gibbs steps are proposed to perform a Bayesian analysis for change-point constant hazard function models considering different prior densities for the parameters and censored survival data. We also present some generalizations for the comparison of two treatments. The methodology is illustrated with some examples.