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Fitting mixtures of linear regressions
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Autores
Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Mixture of linear regressions Classification EM algorithm
Contexto Educativo
Citação
Journal of Statistical Computation and Simulation, Vol. 80, No. 2, February 2010, 201–225