TY: CONF
T1 - Comparison of Mixture and Classification Maximum Likelihood Approaches in Poisson Regression Models
A1 - Faria, Susana
A1 - Soromenho, Gilda
N2 - 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.
UR - http://repositorio.ul.pt/handle/10451/4713
Y1 - 2008
PB - No publisher defined