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Title: Fitting mixtures of linear regressions
Authors: Faria, Susana
Soromenho, Gilda
Keywords: Mixture of linear regressions
Classification EM algorithm
Issue Date: Feb-2010
Publisher: Taylor & Francis
Citation: Journal of Statistical Computation and Simulation, Vol. 80, No. 2, February 2010, 201–225
Abstract: In most applications, the parameters of a mixture of linear regression models are estimated by maximum likelihood using the expectation maximization (EM) algorithm. In this article, we propose the comparison of three algorithms to compute maximum likelihood estimates of the parameters of these models: the EM algorithm, the classification EM algorithm and the stochastic EM algorithm. The comparison of the three procedures was done through a simulation study of the performance (computational effort, statistical properties of estimators and goodness of fit) of these approaches on simulated data sets. Simulation results show that the choice of the approach depends essentially on the configuration of the true regression lines and the initialization of the algorithms.
Peer review: yes
ISSN: 1563-5163
Appears in Collections:IE - GIPE - Artigos em Revistas Internacionais

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