Expectation Parameters in the Poisson Mixture Regression Model for Latent Class by Applying Genetic Algorithm and Maximization Algorithm

Abstract

In a Poisson mixture regression model for latent class, observations come from different sub-sources or classes, and the observed data are assumed to be generated by a specific (finite) mixture of unobserved or latent classes. The problem lies in the optimal assignment of observations to their respective classes. This requires sophisticated methods for estimating the parameters in the model. Usually, the model parameters are estimated by the conventional EM algorithm. The research aims to compare the EM algorithm and the genetic algorithm GA. Using simulation, the two algorithms were compared based on the MSE criterion, with different sample sizes (n = 50, 90, 120) and three scenarios (S1, S2, S3) for default values of the parameters. The results showed the superiority of the GA genetic algorithm over the EM algorithm, as the GA genetic algorithm gave the lowest MSE values.   Paper type: Research paper