EM and beyond |
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Authors: | Donald B. Rubin |
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Affiliation: | (1) Department of Statistics, Harvard University, One Oxford Street, 02138 Cambridge, MA |
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Abstract: | 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.A preliminary version of this article was the Keynote Address at the 1987 European Meeting of the Psychometric Society June 24–26, 1987 in Enschede, The Netherlands. The author wishes to thank the editor and reviewers for helpful comments. |
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Keywords: | data augmentation Gibbs sampler missing data Monte Carlo methods multiple imputation sampling importance resampling simulation techniques stochastic relaxation |
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