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Academic & Professional Books  Reference  Data Analysis & Modelling  Data Analysis & Statistics

Generalized Additive Models An Introduction with R

By: Simon N Wood(Author)
476 pages, 120 b/w illustrations
Generalized Additive Models
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  • Generalized Additive Models ISBN: 9781498728331 Edition: 2 Hardback Jun 2017 Usually dispatched within 5 days
    £64.99
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About this book Contents Customer reviews Biography Related titles

About this book

The second edition of this popular book provides a thorough grounding in linear, generalized linear and mixed models before providing a framework for GAMs. Although the material is technical, the author conveys the information in an accessible way with an eye to applications throughout. With approximately 100 extra pages, the new edition includes recent developments in methodology, more material on mixed models and smoothing, and substantially more examples that cover functional data, survival analysis and location scale models.

Contents

- Linear models
- Inferential Basics
- Generalized Linear Models
- Mixed Models
- Introducing GAMs
- Basis Penalty Smoothing
- Some Theory for GAMs
- GAMs in Practice with mgcv
- Appendices

Customer Reviews

Biography

Simon N. Wood, PhD, is a professor in the Department of Mathematical Sciences at the University of Bath, UK

By: Simon N Wood(Author)
476 pages, 120 b/w illustrations
Media reviews

Reviews of the first edition:

"This is an amazing book. The title is an understatement. Certainly the book covers an introduction to generalized additive models (GAMs), but to get there, it is almost as if Simon has left no stone unturned. In chapter 1 the usual 'bread and butter' linear models is presented boldly. Chapter 2 continues with an accessible presentation of the generalized linear model that can be used on its own for a separate introductory course. The reader gains confidence, as if anything is possible, and the examples using software puts modern and sophisticated modeling at their fingertips. I was delighted to see the presentation of GAMs uses penalized splines – the author sorts through the clutter and presents a well-chosen toolbox. Chapter 6 brings the smoothing/GAM presentation into contemporary and state-of-the-art light, for one by making the reader aware of relationships among P-splines, mixed models, and Bayesian approaches. The author is careful and clever so that anyone at any level will have new insights from hispresentation. This book modernizes and complements Hastie and Tibshirani's landmark book on the topic."
– Professor Brian D. Marx, Louisiana State University, USA

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