Comparison of EM and SEM Algorithms in Poisson Regression Models: A Simulation Study

Abstract

In this work, we propose to compare two algorithms to compute maximum likelihood estimates for the parameters of a mixture Poisson regression model: the EM algorithm and the Stochastic EM algorithm. The comparison of the two procedures was done through a simulation study of the performance of these approaches on simulated data sets and real data sets. Simulation results show that the choice of the approach depends essentially on the overlap of the regression lines. In the real data case, we show that the Stochastic EM algorithm resulted in model estimates that best fit the regression model.