应用与计算数学杂志

An Empirical Study of Generalized Linear Model for Count Data

Abstract

Muritala Abdulkabir, Udokang Anietie Edem, Raji Surajudeen Tunde and Bello Latifat Kemi

This paper deals with an empirical study of generalized linear model (GLM) for count data. In particular, Poisson regression model which is also known as generalized linear model for Poisson error structure has been widely used in recent years; it is also used in modeling of count and frequency data. Quasi Poisson model was employ for handling over and under dispersion which the data was found to be over dispersed and another way of handling over dispersion is negative binomial regression model. In this study, the two regression model were compare using the Akaike information criterion (AIC), the model with minimum AIC shows the best which implies the Poisson regression model.