This is an amazing book. The title is an understatement. Certainly the book covers an introduction to generalized additive models (GAMs), but to get there, it is almost as if Simon has left no stone unturned. In chapter 1 the usual 'bread and butter' linear models is presented boldly. Chapter 2 continues with an accessible presentation of the generalized linear model that can be used on its own for a separate introductory course. The reader gains confidence, as if anything is possible, and the examples using software puts modern and sophisticated modeling at their fingertips. I was delighted to see the presentation of GAMs uses penalized splines - the author sorts through the clutter and presents a well-chosen toolbox. Chapter 6 brings the smoothing/GAM presentation into contemporary and state-of-the-art light, for one by making the reader aware of relationships among P-splines, mixed models, and Bayesian approaches. The author is careful and clever so that anyone at any level will have new insights from his presentation. This book modernizes and complements Hastie and Tibshirani's landmark book on the topic.
-- - Professor Brian D. Marx, Louisiana State University, USA
This attractively written advanced level text shows its style by starting with the question 'How old is the universe?'.
It serves also as a manual for the author's mgcv package, which is one of the R's recommended packages.
The style and emphasis, and the attention to practical data analysis issue, make this a highly appealing volume.
I strongly recommend this book.
-John Maindonald, Australian National University, Journal of Statistical Software, Vol. 16, July 2006