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Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
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Autores
Orientador(es)
Resumo(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.
Descrição
Palavras-chave
Simulation study EM algorithm Mixture Poisson Regression Models Classification EM algorithm
Contexto Educativo
Citação
Compstat 2008-Proceedings in Computational Statistics, Vol. 2