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
Generalized Additive Models: An Introduction with R
โ Scribed by Simon N. Wood
- Publisher
- Chapman and Hall/CRC
- Year
- 2017
- Tongue
- English
- Leaves
- 497
- Series
- Chapman & Hall/CRC Texts in Statistical Science
- Edition
- 2
- Category
- Library
No coin nor oath required. For personal study only.
โฆ 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 bookโs 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.
โฆ Subjects
Probability & Statistics;Applied;Mathematics;Science & Math;Statistics;Mathematics;
๐ SIMILAR VOLUMES
<P>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
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 b
<P>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
<p><p>This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully