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

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  • Kuroda, Masahiro
  • Sakakihara, Michio
  • Geng, Zhi

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.

Suggested Citation

  • Kuroda, Masahiro & Sakakihara, Michio & Geng, Zhi, 2008. "Acceleration of the EM and ECM algorithms using the Aitken [delta]2 method for log-linear models with partially classified data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2332-2338, October.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:15:p:2332-2338
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    References listed on IDEAS

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    1. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
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    1. Kwun Chuen Gary Chan, 2017. "Acceleration of Expectation-Maximization algorithm for length-biased right-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 102-112, January.

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