Improving the EM Algorithm
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.
KeywordsFisher Information Heuristic Approximation Likelihood Surface Complete Data Likelihood Good Convergence Behavior
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