Variable subset selection via GA and information complexity in mixtures of Poisson and negative binomial regression models

Abstract: Count data, for example the number of observed cases of a disease in a city, often arise in the fields of healthcare analytics and epidemiology. In this paper, we consider performing regression on multivariate data in which our outcome is a count. Specifically, we derive log-likelihood functions for finite mixtures of regression models involving counts that come from a Poisson distribution, as well as a negative binomial distribution when the counts are significantly overdispersed. Within our proposed modeling framework, we carry out optimal component selection using the information criteria scores AIC, BIC, CAIC, and ICOMP. We demonstrate applications of our approach on simulated data, as well as on a real data set of HIV cases in Tennessee counties from the year 2010. Finally, using a genetic algorithm within our framework, we perform variable subset selection to determine the covariates that are most responsible for categorizing Tennessee counties. This leads to some interesting insights into the traits of counties that have high HIV counts.