· 2017
Generalized Additive Models
- 8,008citations
- 2017year
Short summary
This book serves as a leading, introductory reference for Generalized Additive Models (GAMs), offering practical examples and software implementation.
AI-generated from the title and abstract; the full text is not read.
Key points
- Establishes itself as a leading, introductory reference for Generalized Additive Models (GAMs).
- Provides necessary background in linear models, linear mixed models, and GLMs.
- Focuses on practical aspects of GAMs using penalized regression splines and R software.
- Includes extensive exercises with solutions for self-study or course use.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source., 2017 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics