- Tapa dura: 410 páginas
- Editor: Chapman and Hall/CRC; Edición: 1 (21 de septiembre de 2016)
- Colección: Chapman & Hall/CRC Texts in Statistical Science
- Idioma: Inglés
- ISBN-10: 1584884746
- ISBN-13: 978-1584884743
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
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Generalized Additive Models: An Introduction with R (Chapman & Hall/CRC Texts in Statistical Science) (Inglés) Tapa dura – 21 sep 2016
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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
Reseña del editor
Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models.
Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions.
The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix.
Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.Ver Descripción del producto
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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.