Fast Algorithms for Estimating Mixture Parameters
{'Code': '03040109', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alvin I. Thaler', 'PO_EMAI': None, 'PO_PHON': None}
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.
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{'Code': '1271', 'Text': 'COMPUTATIONAL MATHEMATICS'}
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