- Series: Chapman & Hall/CRC Texts in Statistical Science (Book 66)
- Hardcover: 410 pages
- Publisher: Chapman and Hall/CRC; 1 edition (February 27, 2006)
- Language: English
- ISBN-10: 1584884746
- ISBN-13: 978-1584884743
- Product Dimensions: 6.5 x 1 x 9.2 inches
- Shipping Weight: 1.6 pounds
- Average Customer Review: 4 customer reviews
- Amazon Best Sellers Rank: #1,234,882 in Books (See Top 100 in Books)
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Generalized Additive Models: An Introduction with R (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition
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"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 hispresentation. This book modernizes and complements Hastie and Tibshirani's landmark book on the topic." -- - Professor Brian D. Marx, Louisiana State University, USA
About the Author
Simon N. Wood is a professor of Statistics at the University of Bath, UK.
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- The PQL algorithm used for fitting GAMM has been brought into question before, especially for binary data where the resulting variance component parameter estimates are highly biased (see for example Breslow's Whither PQL?) to the point that many do not recommend using PQL for binary data (you can use a Bayesian model instead in this case). The book makes no mention of this and only focuses on the diagnostics of binary data. I believe this issue should be brought up with at least a brief section on optional methods of fitting the GAMM.
- Technically GAM models can use any type of basis function, not just splines, so the title of the book is a bit misleading
- (November 2012, update) I found myself using the cairo temperature example in a time series course, when discussing nonparametric based methods (including mixed models) as alternatives to more traditional ARIMA models. To my surprise, I found strong autocorrelation still present in the final model proposed in the book for the temperature data. Although the example is perhaps intended strictly for academic purposes, this finding was quite disappointing.