Repositório Colecção:
http://hdl.handle.net/10451/2027
2017-12-18T14:57:35ZComparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
http://hdl.handle.net/10451/4713
Título: Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
Autor: Faria, Susana; Soromenho, Gilda
Resumo: 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.2008-08-01T00:00:00Z