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Statistical Methods in Medical Research
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EM algorithms without missing data

Mark P Becker

Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, mbecker{at}umich.edu

Ilsoon Yang

Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts

Kenneth Lange

Departments of Biostatistics and Mathematics, University of Michigan, Ann Arbor, Michigan, USA

Most problems in computational statistics involve optimization of an objective function such as a loglikelihood, a sum of squares, or a log posterior function. The EM algorithm is one of the most effective algorithms for maximization because it iteratively transfers maximization from a complex function to a simple, surrogate function. This theoretical perspective clarifies the operation of the EM algorithm and suggests novel generalizations. Besides simplifying maximization, optimization transfer usually leads to highly stable algorithms with well-understood local and global convergence properties. Although convergence can be excruciatingly slow, various devices exist for accelerating it. Beginning with the EM algorithm, we review in this paper several optimization transfer algorithms of substantial utility in medical statistics.

Statistical Methods in Medical Research, Vol. 6, No. 1, 38-54 (1997)
DOI: 10.1177/096228029700600104
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