Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/26551

TitleComparison of mixture and classification maximum likelihood approaches in poisson regression models
Author(s)Faria, Susana
Soromenho, Gilda
KeywordsMaximum likelihood estimation
EM algorithm
Classification EM algorithm
Mixture poisson regression models
Simulation study
Issue dateAug-2008
PublisherSpringer
Abstract(s)In this work, we propose to compare two algorithms to compute maximum likelihood estimators of the parameters of a mixture Poisson regression models. To estimate these parameters, we may use the EM algorithm in a mixture approach or the CEM algorithm in a classification approach. The comparison of the two procedures was done through a simulation study of the performance of these approaches on simulated data sets in a target number of iterations. Simulation results show that the CEM algorithm is a good alternative to the EM algorithm for fitting Poisson mixture regression models, having the advantage of converging more quickly.
TypeConference paper
URIhttp://hdl.handle.net/1822/26551
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

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