EM algorithms without missing data.

M P Becker; I Yang; K Lange

doi:10.1177/096228029700600104

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EM algorithms without missing data.

M P Becker·I Yang·K Lange

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Abstract

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.

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Algorithms Biometry Data Interpretation, Statistical Diagnosis Humans Likelihood Functions Linear Models Logistic Models Sensitivity and Specificity Tomography, X-Ray Computed Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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M P Becker, I Yang, K Lange. 1997. EM algorithms without missing data.. https://doi.org/10.1177/096228029700600104

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