ⅡRCT 下韦布尔分布参数多变点的贝叶斯估计  

BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION

作　　者：何朝兵[1] 

机构地区：[1]安阳师范学院数学与统计学院,安阳455000

出　　处：《机械强度》2016年第3期522-525,共4页Journal of Mechanical Strength

基　　金：国家自然科学基金(61174099);河南省高等学校重点科研项目(16A110001)资助~~

摘　　要：利用逆变换法添加缺损的寿命数据,获得了带有不完全信息随机截尾试验下韦布尔分布的完全数据似然函数。得到了变点位置参数等未知参数的满条件分布。对各参数分别进行Gibbs抽样。把Gibbs样本的均值作为参数的贝叶斯估计。给出了MCMC方法的具体步骤。随机模拟的结果表明估计的精度较高,效果较好。The complete-data likelihood function of Weibull distribution with multiple change points for IIRCT is obtained by filling in missing life data using inverse transformation method. The full conditional distributions of change-point positions and other unknown parameters are obtained. Every parameter is sampled by Gibbs sampler. and the means of Gibbs samples are taken as Bayesian estimations of the parameters. The concrete steps of MCMC methods are given. The random simulation results show that the estimations are fairly accurate and the effect is good.

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]