On Aitken's Method and Other Approaches for Accelerating Convergence of the Em Algorithm

The EM algorithm of Dempster, Laird, and Rubin (1977) is a broadly applicable approach that has been widely applied to the iterative computation of maximum likelihood estimates in a variety of incomplete-data problems. A criticism that has been levelled at the EM algorithm is that its convergence can be quite slow. Unfortunately, methods to accelerate the EM algorithm do tend to sacriice the simplicity it usually enjoys. In this paper, we review some available methods for accelerating convergence of the EM algorithm. In particular, we consider the use of the multivariate version of Aitken's method for EM acceleration. Other methods to be considered include the conjugate gradient approach of Jamshidian and Jennrich (1993) and the quasi-Newton approach of Lange (1995b). We also consider the recently proposed ECME algorithm of Liu and Rubin (1994).