Since our first edition of this book, many developments in statistical mod­ elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv­ ate and illustrate concepts with examples using real data, and most data sets are available on http:/ f stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.

The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis from areas like econometrics, biometrics, and and social sciences.

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 and Hidden Markov Models.- 9. Survival Models.- 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.- B. Software for Fitting Generalized Linear Models and Extensions.- Author Index.