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Generalized Additive Models: An Introduction with R (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition

4.3 out of 5 stars 4 customer reviews
ISBN-13: 860-1416117287
ISBN-10: 1584884746
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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 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

Anthony Award winning author of a dozen books as well as over 150 published stories and articles, Simon Wood is an ex race car driver, a licensed pilot, an animal rescuer, an endurance cyclist, and an occasional private investigator. Having dealt with dyslexia from an early age, Wood has been met with rave reviews for his previous publications, including Working Stiffs, We All Fall Down, Paying the Piper, Dragged into Darkness, Asking for Trouble, and The Fall Guy, among numerous others published under his horror pseudonym, Simon Janus. Originally from the UK, Wood moved to the US in 98 to share his world with his American wife, Julie and a longhaired dachshund and four cats.
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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: 6.5 x 1.1 x 9.3 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: #751,917 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

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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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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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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Format: Hardcover Verified Purchase
A comprehensive book containing both high level overview and low level rigour.
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