Fast Algorithms for Estimating Mixture Parameters

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This study is concerned with the development of fast algorithms for estimating mixture parameters associated with finite mixture densities that arise in many statistical models. Estimating the parameters is often difficult and undesirably time- consuming. The central objective of this research is to determine fast iterative procedures for approximating maximum- likelihood estimates of these parameters. Two broad types of iterative methods are proposed. The first type consists of special quasi-Newton methods. They are designed to take advantage of the special structure of the log-likelihood function. The second type consists of modifications of the popular EM algorithm. They are intended to exhibit accelerated convergence while preserving many of the advantages of the EM algorithm.