Comparison of mixture and classification maximum likelihood approaches in poisson regression models

Autor:	Faria, Susana, Soromenho, Gilda
Přispěvatelé:	Universidade do Minho
Jazyk:	angličtina
Rok vydání:	2008
Předmět:	Mixture poisson regression models Simulation study ComputingMethodologies_PATTERNRECOGNITION Classification EM algorithm Maximum likelihood estimation EM algorithm
Popis:	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. Fundação para a Ciência e a Tecnologia (FCT)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=od_______307::86b9be69792e30e5213ae3d79c4e00dd https://hdl.handle.net/1822/26551 Zobrazit plný text záznamu