EM algorithms without missing data.
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.
Becker
M P
MP
Department of Biostatistics, University of Michigan, Ann Arbor 48109-2029, USA. mbecker@umich.edu
Yang
I
I
Lange
K
K
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D003933:Diagnosis; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data