DSpace Collection:http://hdl.handle.net/10451/20272013-06-18T07:30:38Z2013-06-18T07:30:38ZComparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression ModelsFaria, SusanaSoromenho, Gildahttp://hdl.handle.net/10451/47132011-12-27T14:41:25Z2008-08-01T00:00:00ZTitle: 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