Estimation Parameters in the fuzzy class Poisson mixture regression model

Abstract

In the Poisson mixture regression for the fuzzy class model, observations originate from distinct sub-sources or classes due to the problem of heteroscedasticity. The underlying assumption posits that the observed data stem from a finite mixture of fuzzy classes. The primary challenge lies in achieving the optimal assignment of observations to their respective categories, necessitating the utilization of sophisticated methods for parameter estimation within the model. This research paper explores the estimation of parameters in a Poisson mixture regression model designed for the fuzzy class. Leveraging the FCML (Fuzzy Classification Maximum Likelihood algorithm) and genetic algorithm methodologies, we conducted simulations to assess the accuracy and efficacy of parameter estimation. Our findings reveal a clear superiority of the genetic algorithm over the FCML algorithm, as evidenced by the Mean Square Error (MSE) criterion. The genetic algorithm consistently produced lower MSE values, indicating more precise and reliable parameter estimates compared to the FCML algorithm. This research underscores the potential of the genetic algorithm as a robust and effective tool for parameter estimation in complex statistical models, offering researchers an alternative approach for tackling challenges in the estimation of parameters within the context of the fuzzy class