Paper detail

Exponential Dispersion Models for Overdispersed Zero-Inflated Count Data

We consider three new classes of exponential dispersion models of discrete probability distributions which are defined by specifying their variance functions in their mean value parameterization. In a previous paper (Bar-Lev and Ridder, 2020a), we have developed the framework of these classes and proved that they have some desirable properties. Each of these classes was shown to be overdispersed and zero inflated in ascending order, making them as competitive statistical models for those in use in statistical modeling. In this paper we elaborate on the computational aspects of their probability mass functions. Furthermore, we apply these classes for fitting real data sets having overdispersed and zero-inflated statistics. Classic models based on Poisson or negative binomial distributions show poor fits, and therefore many alternatives have already proposed in recent years. We execute an extensive comparison with these other proposals, from which we may conclude that our framework is a flexible tool that gives excellent results in all cases. Moreover, in most cases our model gives the best fit.