• Title: Generalised additive models an introduction
• Author: Simon N. Wood
• Series:  Chapman & Hall / CRC Texts in Statistical Science
• Hardcover:  496 pages
• Publisher:  Chapman and Hall / CRC; 2 edition (June 2, 2017)
• Language:  English
• ISBN-10:  1498728332
• ISBN-13:  978-1498728331
  

"A well-written book providing in-depth and comprehensive coverage of regulatory models from linear models through generalized linear and mixed models to generalized additive models. The book stands by by placing weight on geometric intuition and numerically efficient algorithms algorithms, but most only by Provide many worked-through application examples with details on model choice as well as accompanying R-code. Compared to the first edition, many new developments are included, from improved inference in generalized additive models to extensions such as response distributions outside the exponential family. As the book includes many advanced topics and the necessary theory but develops everything from the basics, it will be of interest to statistical gene and practitioners alike.It will be a handy reference book for anyone using the popular mgcv R package and could also be used as an accompanying textbook for a series of regress for graduate or advanced undergraduate students. "
- Sonja Greven , Professor, Department of Statistics, Ludwig- Maximilians-Universität München, Munich

"A great book got even better. Simon Wood's focus on splines for fitting GAMs allows for a seamless integration with mixed effects models and gaussian processes, which raisedges the scope of GAMs." This book and the R software are wonderful contributions to statistics and Data science. "
- Trevor Hastie , Stanford University

"The first edition of Simon Wood's Generalized Additive Models appeared in 2006 to wide and well-deserved acclaim. Since then the field has progressized by; in particular Wood himself has made a stunning array of major advances. In his newly revised text, Wood expertly And engagely guides the reader from background material on linear and generalized linear models all the way through the latest developments in generalized additive (mixed) models. For anyone seeking an up-to-date treatment of what smooth models can do, this new edition is Indispensable. " - Philip Reiss , University of Haifa and New York University  

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