http://hdl.handle.net/10451/2027 2013-05-25T12:49:20Z http://hdl.handle.net/10451/4713 Title: Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models Authors: Faria, Susana; Soromenho, Gilda Abstract: 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