Optimization of mixture models: Comparison of different strategies
type: Item
instance of: scholarly article
title: Optimization of mixture models: Comparison of different strategies
zbMATH Open document ID: 1068.65020
DOI: 10.1007/BF03372103
author: André Berchtold
published in: Computational Statistics
publication date: 2005-05-20T00:00:00Z
review text: The main goal of this paper is to compare the performance of several optimisation strategies based on expectation-maximization-type and genetic algorithms, and to determine whether the same algorithm must be used during the entire optimisation phase, or the optimisation should be divided into two stages: an initialisation phase during which optimal starting values are identified, and an optimisation phase with the goal to discover the global maximum. Twenty three different strategies were compared through simulation experiments. The obtained results indicate that two-stage procedures provide the best results, especially when these methods are applied on several sets of initial conditions rather than on one single starting.
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 2169416
zbMATH Keywords: mixture model
zbMATH Keywords: genetic algorithm
zbMATH Keywords: mixture transition distribution model
zbMATH Keywords: expectation-maximization algorithm
zbMATH Keywords: numerical examples
zbMATH Keywords: comparison of methods
zbMATH Keywords: optimisation strategies
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 2169416