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...

In public health research, it is common to follow a cohort of subjects over time, observing a vector of health indicators and a set of covariates at each of many visits. An objective of analysis is to characterize the inter-dependencies, in particular, the feedback of one response upon another...

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,...

The maximum likelihood estimation in a regression model with heteroscedastic errors is considered. When the design matrices in the model are inappropriately specified, the maximum likelihood estimates of the variances of certain observations are found to be zero irrespective of the observed...