In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. Besides the inclusion of covariates, spatial effects are incorporated and modelled using a proper Gaussian conditional autoregressive prior based on Pettitt et al. (2002). Apart from...

Overdispersion in count data regression is often caused by neglection or inappropriate modelling of individual heterogeneity, temporal or spatial correlation, and nonlinear covariate effects. In this paper, we develop and study semiparametric count data models which can deal with these issues by...

The book provides graduate students and researchers with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the...

An analysis of the effects of right-to-carry laws on crime requires particular distributional and structural considerations. First, due to the count nature of crime data and the low number of expected instances per observation in the most appropriate data, last-squares methods yield unreliable...