Generalized Additive Models : An Introduction with R, Second Edition

Simon N. Wood

ISBN 10: 1498728332 / ISBN 13: 9781498728331

Published by Chapman And Hall/CRC, 2017

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9781498728331

Bibliographic Details

• Title: Generalized Additive Models : An Introduction with R, Second Edition
• Publisher: Chapman And Hall/CRC
• Publication Date: 2017
• Language: English
• ISBN 10: 1498728332
• ISBN 13: 9781498728331
• Binding: Buch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 240W x 161H x 31D millimeters 240 millimeters width by 161 millimeters height by 31 millimeters depth
• Weight: 907 grams

About this title

Synopsis

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.

About the Author

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