Estimation of Count Data using Mixed Poisson, Generalized Poisson and Finite Poisson Mixture Regression Models

摘要

This study relates the Poisson, mixed Poisson (MP), generalized Poisson (GP) and finite Poisson mixture (FPM) regression models through mean-variance relationship, and suggests the application of these models for overdispersed count data. As an illustration, the regression models are fitted to the US skin care count data. The results indicate that FPM regression model is the best model since it provides the largest log likelihood and the smallest AIC, followed by Poisson-Inverse Gaussion (PIG), GP and negative binomial (NB) regression models. The results also show that NB, PIG and GP regression models provide similar results.