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Generalized Additive Models: An Introduction with R (Chapman & Hall/CRC Texts in Statistical Science) Hardcover – February 27, 2006

ISBN-13: 978-1584884743 ISBN-10: 1584884746 Edition: 1st

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Product Details

  • 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: 9.3 x 6.5 x 1.1 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #219,613 in Books (See Top 100 in Books)

Editorial Reviews

Review

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

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Most Helpful Customer Reviews

16 of 17 people found the following review helpful By Anonymous statman on May 29, 2008
Format: Hardcover Verified Purchase
The author has made a great job on making GAM accessible to a wide audience through his exposition in this work. The clear (not detailed) presentation of generalized additive models should be very helpful to many searching for models more flexible than a parametric model. The good intuitive explanations are complemented with good examples to cover the theory and the computation. As much as I would like to give the book 5 stars, I find some flaws in the book which could catch the inexperienced off balance.
- 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.
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11 of 12 people found the following review helpful By Edward Hess on July 7, 2009
Format: Hardcover Verified Purchase
This text is clearly written and provides a lot of practical examples in R. It also provides a nice buildup to GAMs providing both theoretical and applied background in linear models, generalized linear models, and mixed models. It also includes a nice collection of illustrations to help aid understanding. At my level (I'm pursuing a Master's in Biostatistics) this has been very useful, and has helped to tie things together. This easily ranks among the best math texts I've encountered. Anyway, kudos to the author.
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10 of 11 people found the following review helpful By Michael R. Chernick on November 11, 2008
Format: Hardcover
Since the excellent original text on generalized additive models by Hastie and Tibshirani, I know of no other major statistical text devoted to this important topic. This book provides a lucid description of the methods and applications of generalized additive models (GAMs) and related advanced methods such as generalized linear models. It is of course more up-to-date than the Hastie-Tibshirani text and is more detailed. It also has the nice feature of providing an introduction to R programming and it illustrates the application of GAMs using R.
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2 of 3 people found the following review helpful By Andrew Robinson on June 27, 2012
Format: Hardcover
I recommend this book to pretty much anyone I talk to who wants to know about linear models and their extensions. The explanations of LM, GLM, and so on are the clearest that I have seen. Great book!
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