EM AND BEYOND

被引：62

作者：

RUBIN, DB

机构：

[1] Department of Statistics, Harvard University, Cambridge, 02138, MA, One Oxford Street

来源：

PSYCHOMETRIKA | 1991年 / 56卷 / 02期

关键词：

DATA AUGMENTATION; GIBBS SAMPLER; MISSING DATA; MONTE-CARLO METHODS; MULTIPLE IMPUTATION; SAMPLING IMPORTANCE RESAMPLING; SIMULATION TECHNIQUES; STOCHASTIC RELAXATION;

D O I：

10.1007/BF02294461

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The basic theme of the EM algorithm, to repeatedly use complete-data methods to solve incomplete data problems, is also a theme of several more recent statistical techniques. These techniques-multiple imputation, data augmentation, stochastic relaxation, and sampling importance resampling-combine simulation techniques with complete-data methods to attack problems that are difficult or impossible for EM.

引用

页码：241 / 254

页数：14

共 45 条

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