1 Introduction.- 2 Modelling and analysis of cross-sectional data: a review of univariate generalized linear models.- 3 Models for multicategorical responses: multivariate extensions of generalized linear models.- 4 Selecting and checking models.- 5 Semi- and nonparametric approaches to regression analysis.- 6 Fixed parameter models for time series and longitudinal data.- 7 Random effects models.- 8 State space models.- 9 Survival models.- Appendix A.- A.1 Exponential families and generalized linear models.- A.2 Basic ideas for asymptotics.- A.3 EM-algorithm.- A.4 Numerical integration.- A.5 Monte Carlo methods.- Appendix B Software for fitting generalized linear models.- References.- Author Index.

Concerned with the use of generalised linear models for univariate and multivariate regression analysis, this is a detailed introductory survey of the subject, based on the analysis of real data drawn from a variety of subjects such as the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account.