This specific ISBN edition is currently not available.View all copies of this ISBN edition:
Imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these flexible tools. The author's approach is based on a framework of penalized regression splines, and he builds the necessary foundation through motivating chapters on linear and generalized linear models.
"synopsis" may belong to another edition of this title.
"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, USAAbout the Author:
Simon N. Wood is a professor of Statistics at the University of Bath, UK.
"About this title" may belong to another edition of this title.
Book Description Chapman and Hall/CRC, 2006. Hardcover. Condition: New. 1. Seller Inventory # DADAX1584884746
Book Description Chapman and Hall/CRC, 2006. Hardcover. Condition: New. Never used!. Seller Inventory # P111584884746