左截断右删失数据下泊松分布参数多变点的贝叶斯估计  

Bayesian estimation of multiple variable points in Poisson distributive parameters with left truncated and right censored data

作　　者：何朝兵[1] 刘华文[2] 

机构地区：[1]安阳师范学院数学与统计学院,河南安阳455000 [2]山东大学数学学院,山东济南250100

出　　处：《兰州理工大学学报》2014年第4期136-140,共5页Journal of Lanzhou University of Technology

基　　金：国家自然科学基金(61174099);河南省教育厅自然科学基金(2011B110001)

摘　　要：通过添加缺损的寿命变量数据得到左截断右删失数据下泊松分布的完全数据似然函数.给出变点位置和其它参数的满条件分布.利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法对各参数的满条件分布分别进行抽样,介绍MCMC方法的实施步骤.把Gibbs样本的均值作为各参数的贝叶斯估计.随机模拟试验的结果表明各参数贝叶斯估计的精度都较高.By adding the missing data of life variable, the complete data likelihood function of Poisson distribution of truncated and censored data is obtained. The full-condition distributions of change-point positions and other parameters are given. Every parameter is sampled from the full-condition distributions respectively, by using MCMC method of Gibbs sampling combined with Metropolis-Hastings algorithm, and the implementation procedure of MCMC method is introduced in detail. The means of Gibbs samples are taken as Bayesian estimations of the parameters. The random simulation test results show that Bayesian estimations of the parameters are fairly accurate.

关 键 词：完全数据似然函数 满条件分布 MCMC方法 GIBBS抽样 Metropolis-Hastings算法 

分 类 号：O213.2[理学—概率论与数理统计] O212.8[理学—数学]