缺失数据下多元正态模型Monte Carlo EM算法  

Monte Carlo EM Algorithm for Multivariate Normal Distribution under Missing Data

作　　者：王继霞[1] 刘次华[2] 

机构地区：[1]河南师范大学数学与信息科学学院,河南新乡453007 [2]华中科技大学数学与统计学院,湖北武汉430074

出　　处：《郑州大学学报（理学版）》2011年第3期59-61,共3页Journal of Zhengzhou University:Natural Science Edition

基　　金：国家自然科学基金资助项目;编号10671057;河南省教育厅软科学研究计划;编号2010B110013

摘　　要：研究含有缺失数据的多元正态模型参数的极大似然估计问题,利用Monte Carlo EM算法求得多元正态模型参数的迭代解,并证明了此迭代解收敛到最优解,且其收敛速度是二阶的.Maximum likelihood estimations of the parameters of multivariate normal distribution models under missing data were studied. The iterative solution of the parameters of multivariate normal distribution models were obtained through the Monte Carlo EM algorithm and this solution converge to the optimum solution were proved and the convergence rate of this solution was secondary.

关 键 词：多元正态模型 缺失数据 EM算法 MONTE Carlo EM算法 Newton-Raphson算法 

分 类 号：O212.1[理学—概率论与数理统计]