Improving the EM Algorithm
Copyright policy )Improving the EM Algorithm
Summary: A modification of the EM algorithm is proposed for situations in which the maximization of the ``complete data'' likelihood function does not have a closed-form solution. Self-consistency of the modified algorithm is established. Application to carcinogenicity experiments is illustrated, and the results of a simulation study comparing the original and modified versions indicate that use of the proposed modification can lead to significant computational savings.
incomplete data, self-consistency, carcinogenicity experiments, Probabilistic methods, stochastic differential equations, modification of the EM algorithm, simulation study, Applications of statistics to biology and medical sciences; meta analysis
incomplete data, self-consistency, carcinogenicity experiments, Probabilistic methods, stochastic differential equations, modification of the EM algorithm, simulation study, Applications of statistics to biology and medical sciences; meta analysis
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