Bibliografía: p. 455-465.

Preface -- 1. Linear models -- 2. Linear models -- 3. Generalized linear models -- 4. Introducing GAMs -- 5. Smoothers -- 6. GAM theory -- 7. GAMs in practice: mgcv -- A: Maximum likelihood estimation -- B: Some matrix algebra -- C: Solutions to exercises -- Bibliography.

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models.

The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.