Utilize este identificador para referenciar este registo: http://hdl.handle.net/10451/4682
Título: Fitting mixtures of linear regressions
Autor: Faria, Susana
Soromenho, Gilda
Palavras-chave: Mixture of linear regressions
Classification EM algorithm
Data: Fev-2010
Editora: Taylor & Francis
Citação: Journal of Statistical Computation and Simulation, Vol. 80, No. 2, February 2010, 201–225
Resumo: 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
URI: http://hdl.handle.net/10451/4682
ISSN: 1563-5163
Aparece nas colecções:IE - GIPE - Artigos em Revistas Internacionais

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