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Improving the EM Algorithm

In: Computing Science and Statistics

Author

Listed:
  • David Lansky

    (Cornell University, Biometrics Unit)

  • George Casella

    (Cornell University, Biometrics Unit)

Abstract

The EM algorithm is often a practical method for obtaining maximum likelihood estimates. For the vector parameter case, we provide a faster method than Meng and Rubin (1989) for obtaining the derivative of the EM mapping, which can be used to obtain the observed variance-covariance matrix. Our method exhibits good behavior for a simple example. Aitken’s acceleration is commonly used to speed convergence of EM near a solution. Because Aitken’s acceleration often fails to converge we propose a mixture of EM and Aitken accelerated EM which satisfies the generalized EM (GEM) criteria, assuring convergence. We show that such a mixture sequence exists and demonstrate good convergence behavior for a heuristic approximation to this mixture.

Suggested Citation

  • David Lansky & George Casella, 1992. "Improving the EM Algorithm," Springer Books, in: Connie Page & Raoul LePage (ed.), Computing Science and Statistics, pages 420-424, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2856-1_67
    DOI: 10.1007/978-1-4612-2856-1_67
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