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...

Various count data models are applied to data collected from a sample of Norfolk young persons, who were asked how many times they had had sexual intercourse during the previous two-week period. The models take account of the fact that the data are "grouped", meaning that for some observations,...