摘要
通过添加缺损的寿命变量数据得到了左截断右删失数据下Pareto分布相对简单的似然函数,给出了形状参数变点位置和其他参数的满条件分布.利用MCMC方法对参数的满条件分布进行了抽样,把Gibbs样本的均值作为参数的贝叶斯估计.随机模拟的结果表明各参数贝叶斯估计的精度都较高.
By filling in the missing data of the life variable, the relatively simple likelihood function of Pareto distribution for left truncated and right censored data is obtained. The full conditional distributions of change-point positions of shape parameter and other parameters are given. Each parameter is sampled from the full conditional distributions by MCMC method. The means of Gibbs samples are taken as Bayesian estimations of the parameters. The random simulation results show that Bayesian estimations of the parameters are fairly accurate.
出处
《湖南师范大学自然科学学报》
CAS
北大核心
2015年第3期80-84,共5页
Journal of Natural Science of Hunan Normal University
基金
国家自然科学基金资助项目(61174099)
河南省教育厅科学技术研究重点项目(14B110011)
