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. "A well-written book providing in-depth and comprehensive coverage of regression models from linear models through generalized linear and mixed models to generalized additive models. The book stands out by placing weight on geometric intuition and numerically efficient estimation algorithms, but most importantly by providing many worked-through application examples with details on model choice as well as accompanying R-code. Compared to the first edition, many new developments are included, from improved inference in generalized additive models to extensions such as response distributions outside the exponential family. As the book includes many advanced topics and the necessary theory but develops everything from the basics, it will be of interest to statistical researchers and practitioners alike. It will be a handy reference book for anyone using the popular mgcv R package and could also be used as an accompanying textbook for a series of regression courses for graduate or advanced undergraduate students."
-Sonja Greven, Professor, Department of Statistics, Ludwig-Maximilians-Universitat Munchen, Munich
"A great book got even better. Simon Wood's focus on splines for fitting GAMs allows for a seamless integration with mixed effects models and gaussian processes, which enlarges the scope of GAMs considerably. This book and the R software are wonderful contributions to applied statistics and data science."
-Trevor Hastie, Stanford University
"The first edition of Simon Wood's Generalized Additive Models appeared in 2006 to wide and well-deserved acclaim. Since then the field has progressed considerably; in particular Wood himself has made a stunning array of major advances. In his newly revised text, Wood expertly and engagingly guides the reader from background material on linear and generalized linear models all the way through the latest developments in generalized additive (mixed) models. For anyone seeking an up-to-date treatment of what smooth models can do, this new edition is indispensable."
-Philip Reiss, University of Haifa and New York University
"This excellent and well-written book covers a lot more than "merely" GAMs, with the first few chapters providing a pretty comprehensive guide to regression modelling in general. That is a boon for would-be GAM-users from applied fields such as ecology, who sometimes find themselves plunged into the deep end of statistical modelling (GAMs) without much practice in the shallow end. The presentation in this second edition now puts mixed-effect models up-front alongside generalized linear models, presenting GAMs as the glorious fruit of their union, with smooth terms being random effects. This leads to a coherent and extensible modelling framework throughout, which I would describe as broadly Bayesian but not dogmatically so. There is a quiet but consistent emphasis on sound theoretical underpinnings and computational reliability- valuable in the field of smoothing, where ad hoc approaches have been rife, and where inferential principles need to be stretched hard to handle the types of model that can nowadays be fitted. The extensive examples using the mgcv R package are realistic and not over-simplified, and nicely show when enough work is enough. The theory chapters pack enough in to let an advanced user extend the machinery to broader classes of data (from my own experience); and they contain substantial new material, reflecting 10 more years of practical experience and application-driven development, for example to cope with huge datasets. The tools and the theory covered by this book and its predecessor have certainly been a major influence on my own statistical practice over the last 20 years, and I have no doubt they will continue to be."
- Dr. Mark Bravington, Senior research statistician, CSIRO, Australia
"The new edition substantially differs in many respects from the original edition. There are about 80 more pages adding new important results, which have been derived in the last decade. The central change is that linear mixed models theory is now already discussed very early within the second chapter. This is a clever didactical change because it makes the equivalence of smooth regression and random effect models much clearer. There are now sections on adaptive smoothing, SCOP-splines, or soap film smoothers. There is lots of modified and new material in the last section of the book on GAMs in practice: mgcv. Here you can find the analysis of several new data problems and also a section on functional data analysis. Overall the content of the second edition is now presented such that effective teaching and learning is strongly promoted. For practitioners working with the R library mgcv, this second edition describes at length all the actual issues and possibilities of this powerful set of functions. This book is definitely covering the state-of-the-art in modern smooth modelling. I strongly recommend this new edition due to all the reasons I have mentioned above."
-Herwig Friedl, Graz University of Technology, Austria
"This book is so much more than it says in the title! In addition to being my go-to text for generalized additive models, it provides a very clear and concise introduction to linear models, linear mixed models, generalized linear models and generalized additive mixed models. This is supplemented by accessible appendices laying out key results in maximum likelihood theory and the matrix algebra required for the theory covered in the book.
The first edition of this excellent text is one of the books I consult most frequently, both for teaching and research purposes.This second edition substantially updates and expands the scope and the depth of the book. There is a new chapter on mixed effects models that expands on material in the first edition, more on GLMMs, an extended chapter on Smoothers that includes treatment of Gaussian Markov Random fields, and well-organised solutions to exercises.
If you teach courses on linear models, GLMs, GLMMs, GAMs or GAMMs you will find this book a valuable resource for theoretical material, for illustrative applications, for exercises, and as a guide to using the mgcv package in your course. If you do research that may require any of the above methods, you will find that this book provides an invaluable synthesis of the areas, as well as a reference source for the technical detail of the methods.
I know of very few statistics books that combine such an accessible synthesis of a broad area of statistics with the rigor and detail that allows the reader to understand the intricacies of virtually any aspect of the area. Prof Wood has a rare ability to see both the wood and the trees with incisive clarity."
-Prof. David Borchers, University of St Andrews
"The first edition of this book has been one of the most valuable resources both to get fami
Generalized Additive Models: An Introduction with R, Second Edition
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