This is the first text devoted specifically to the negative binomial regression model. Important to researchers desiring to model count response data, the procedure has only recently been added to the capabilities of leading commercial statistical software. However, it is now one of the most common methods used by statisticians to accommodate extra correlation – or overdispersion – when modeling counts. Since most real count data modeling situations appear to involve overdispersion, the negative binomial has been finding increased use among statisticians, econometricians, and researchers who commonly analyze count response data.

This volume will explore both the theory and varieties of the negative binomial. It will also provide the reader with examples using each type of major variation it has undergone. However, of prime importance, the text will also attempt to clarify discrepancies regarding the negative binomial that often appear in the statistical literature. What exactly is a negative binomial model? How does it relate to other models? How is its variance function to be defined? Is it a member of the family of generalized linear models? What is the most appropriate manner by which to estimate parameters? How are parameters to be interpreted, and evaluated as to their worth? What are the limits of its applicability? How has it been extended to form more complex models? These are important questions that have at times found differing answers depending on the author.