高校应用数学学报A辑
高校應用數學學報A輯
고교응용수학학보A집
APPLIED MATHEMATICS A JOURNAL OF CHINESE UNIVERSITIES
2015年
2期
127-138
,共12页
完全数据似然函数%满条件分布%MCMC方法%Gibbs抽样%Metropolis-Hastings算法
完全數據似然函數%滿條件分佈%MCMC方法%Gibbs抽樣%Metropolis-Hastings算法
완전수거사연함수%만조건분포%MCMC방법%Gibbs추양%Metropolis-Hastings산법
complete-data likelihood function%full conditional distribution%MCMC method%Gibbs sampling%Metropolis-Hastings algorithm
通过添加缺失的寿命变量数据，得到了删失截断情形下Weibull分布多变点模型的完全数据似然函数，研究了变点位置参数和形状参数以及尺度参数的满条件分布。利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法得到了参数的Gibbs样本，把Gibbs样本的均值作为各参数的Bayes 估计。详细介绍了MCMC方法的实施步骤。随机模拟试验的结果表明各参数Bayes估计的精度都较高。
通過添加缺失的壽命變量數據，得到瞭刪失截斷情形下Weibull分佈多變點模型的完全數據似然函數，研究瞭變點位置參數和形狀參數以及呎度參數的滿條件分佈。利用Gibbs抽樣與Metropolis-Hastings算法相結閤的MCMC方法得到瞭參數的Gibbs樣本，把Gibbs樣本的均值作為各參數的Bayes 估計。詳細介紹瞭MCMC方法的實施步驟。隨機模擬試驗的結果錶明各參數Bayes估計的精度都較高。
통과첨가결실적수명변량수거，득도료산실절단정형하Weibull분포다변점모형적완전수거사연함수，연구료변점위치삼수화형상삼수이급척도삼수적만조건분포。이용Gibbs추양여Metropolis-Hastings산법상결합적MCMC방법득도료삼수적Gibbs양본，파Gibbs양본적균치작위각삼수적Bayes 고계。상세개소료MCMC방법적실시보취。수궤모의시험적결과표명각삼수Bayes고계적정도도교고。
By filling in the missing data of the life variable, the complete-data likelihood function of Weibull distribution with multiple change points for truncated and censored data is obtained. The full conditional distributions of change-point positions, shape parameters, and scale parameters are studied. Gibbs samples of the parameters are obtaines by MCMC method of Gibbs sampling together with Metropolis-Hastings algorithm, and the means of Gibbs samples are taken as Bayesian estimations of the parameters. The implementation steps of MCMC method are introduced in detail. The random simulation test results show that Bayesian estimations of the parameters are fairly accurate.
