Generalized Additive Models

An Introduction with R, Second Edition

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Generalized Additive Models by Simon N. Wood, CRC Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Simon N. Wood ISBN: 9781498728379
Publisher: CRC Press Publication: May 18, 2017
Imprint: Chapman and Hall/CRC Language: English
Author: Simon N. Wood
ISBN: 9781498728379
Publisher: CRC Press
Publication: May 18, 2017
Imprint: Chapman and Hall/CRC
Language: English

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.

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

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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

More books from CRC Press

Cover of the book Progress in Nonhistone Protein Research by Simon N. Wood
Cover of the book Lubrication Fundamentals, Revised and Expanded by Simon N. Wood
Cover of the book Grasslands of the World by Simon N. Wood
Cover of the book Environmental Management in Construction by Simon N. Wood
Cover of the book Multiple Criteria Decision Making in Supply Chain Management by Simon N. Wood
Cover of the book Multimedia Image and Video Processing by Simon N. Wood
Cover of the book Essential Forensic Pathology by Simon N. Wood
Cover of the book Optical Wideband Transmission Systems by Simon N. Wood
Cover of the book Aqueous Polymeric Coatings for Pharmaceutical Dosage Forms by Simon N. Wood
Cover of the book Diagnosis by Simon N. Wood
Cover of the book Control and Automation of Electrical Power Distribution Systems by Simon N. Wood
Cover of the book Labor Relations in the Public Sector by Simon N. Wood
Cover of the book The Economics of Fire Protection by Simon N. Wood
Cover of the book Renewable Energy Devices and Systems with Simulations in MATLAB® and ANSYS® by Simon N. Wood
Cover of the book Quantum Kinematics And Dynamic by Simon N. Wood
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy