The modeling of count data is of a primary interest in many fields such as insurance, public health, epidemiology, psychology and many other research areas. The Poisson model is most commonly used for modeling such a count data. It assumes that the mean and variance are equal. However, this restriction is violated in many applications because data is often overdispersed. In this case, Poisson distribution ( PD ) underestimates the dispersion of the observed counts. One cause of overdispersion is excess zeroes in the data and detected when the frequency of ‘zero’ is significantly higher than the one predicted by the Poisson model.
