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...

Buy Now From Amazon

Product Review

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.



Similar Products

Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)R for Data Science: Import, Tidy, Transform, Visualize, and Model DataGeneralized Additive Models (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)The Hundred-Page Machine Learning BookBayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)Applied Predictive ModelingThe Book of Why: The New Science of Cause and EffectData Analysis Using Regression and Multilevel/Hierarchical Models