Generalized Additive Models: An Introduction with R, Second Edition

Simon N. Wood

May 30, 2017 by Chapman and Hall/CRC
Textbook - 476 Pages - 145 B/W Illustrations
ISBN 9781498728331 - CAT# K25925
Series: Chapman & Hall/CRC Texts in Statistical Science

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New to the Second Edition:

  • Mixed models are introduced much earlier in the book, in a new Chapter 2 and alongside GLMs in Chapter 3.
  • The range of smoothers covered is substantially enlarged to include adaptive smoothing, P-splines with derivative penalties, Duchon splines, splines on the sphere, Gaussian process smoothers and more.
  • The chapter on GAM theory has been substantially updated, including recent improved methods for estimation and inference as well as methods for large data sets and models.
  • Includes new examples covering topics such as survival modelling, location scale modelling, functional data analysis, spatio-temporal modelling, Bayesian simulation, and more.


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.

Simon N. Wood is a professor of Statistical Science at the University of Bristol, UK, and author of the R package mgcv.


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