Generalized Additive Models: An Introduction with R, Second Edition

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CRC Press, May 18, 2017 - Mathematics - 496 pages

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models.

The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.

 

Selected pages

Contents

Linear Models
1
Linear Mixed Models
61
Generalized Linear Models
101
Introducing GAMs
161
Smoothers
195
GAM theory
249
GAMs in Practice mgcv
325
Maximum Likelihood Estimation
405
Some Matrix Algebra
419
Solutions to Exercises
429
Bibliography
455
Index
467
Copyright

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Generalized Additive Models: An Introduction with R
Simon N. Wood
No preview available - 2017

Common terms and phrases

additive alternative applied approach approximation basis coefficients columns component computed consider constant contains corresponding covariates curve defined degrees of freedom depend derivatives deviance distribution elements error estimated example expected factor Figure fitted fitted values fixed function given gives Hence illustrates increase independent intervals iteration known likelihood linear model marginal mean measure method mixed model model matrix model selection normal Note Notice null observed obtained orthogonal matrix p-value package panel penalized penalty plots possible prediction predictor problem provides random effects regression REML residuals response sample scale score selection shown shows simple simulation smoothing parameter space spline standard statistic step structure suggests tree usually values variables variance vector weights zero

About the author (2017)

Simon N. Wood is a professor of Statistical Science at the University of Bristol, UK, and author of the R package mgcv.

Bibliographic information

QR code for Generalized Additive Models
TitleGeneralized Additive Models: An Introduction with R, Second Edition
Chapman & Hall/CRC Texts in Statistical Science
AuthorSimon N. Wood
Edition2, illustrated
PublisherCRC Press, 2017
ISBN1498728340, 9781498728348
Length496 pages
Subjects ›  › 

Mathematics / Probability & Statistics / General
  
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