Acceleration of the EM and ECM algorithms using the Aitken [delta]2 method for log-linear models with partially classified data

Masahiro Kuroda, Michio Sakakihara and Zhi Geng

Statistics & Probability Letters, 2008, vol. 78, issue 15, 2332-2338

Abstract: In this paper, we discuss the MLEs for log-linear models with partially classified data. We propose to apply the Aitken [delta]2 method of Aitken [Aitken, A.C., 1926. On Bernoulli's numerical solution of algebraic equations. Proc. R. Soc. Edinburgh 46, 289-305] to the EM and ECM algorithms to accelerate their convergence. The Aitken [delta]2 accelerated algorithm shares desirable properties of the EM algorithm, such as numerical stability, computational simplicity and flexibility in interpreting the incompleteness of data. We show the convergence of the Aitken [delta]2 accelerated algorithm and compare its speed of convergence with that of the EM algorithm, and we also illustrate their performance by means of a simulation.

Date: 2008
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