Improving the EM Algorithm

David Lansky and George Casella
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David Lansky: Cornell University, Biometrics Unit
George Casella: Cornell University, Biometrics Unit

A chapter in Computing Science and Statistics, 1992, pp 420-424 from Springer

Abstract: 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.

Keywords: Fisher Information; Heuristic Approximation; Likelihood Surface; Complete Data Likelihood; Good Convergence Behavior (search for similar items in EconPapers)
Date: 1992
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2856-1_67

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DOI: 10.1007/978-1-4612-2856-1_67

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