Cited by 13,852Statistical Methods and InferenceStatistical Methods and ApplicationsStatistical Methods and Bayesian Inference

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Generalized Additive Models: An Introduction with R

Wood, Simon N.·2017·Chapman & Hall/CRC eBooks

DOI 10.1201/9781315370279

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Abstract

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.

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Authors: Wood, S. N.
Corresponding author: Wood, Simon N.
Venue: Chapman & Hall/CRC eBooks
Publisher: Chapman and Hall/CRC
Published: 2017-05-18
DOI: 10.1201/9781315370279
OpenAlex: W3112717819
Cited by: 13,852
References: 0
Citations by year: 2026: 1378 · 2025: 1355 · 2024: 1085 · 2023: 934 · 2022: 976 · 2021: 1373 · 2020: 1048 · 2019: 787 · 2018: 603 · 2017: 556
FWCI: 208.09
Type: book
Language: en
Open access: closed
Locations: Chapman & Hall/CRC eBooks (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: generalized additive models, penalized spline regression, linear mixed models, generalized linear models, linear models, R software, nonparametric regression

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