This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.
"That the authors succeed in conveying their message is largely due to the extremely clear and effective organization of the text. General concepts are introduced, illustrated with relatively simple applications, and then re-examined at a more theoretical level."
-Journal of the Royal Statistical Society, Series C
"This book aims to 'provide an up-to-date survey of current research in additive modelling,' with the 'emphasis on practical rather than theoretical.' With that in mind, the book does extremely well."
-Statistics in Medicine
"Generalized Additive Models is a good starting point for researchers wishing to initiate themselves in the vast and burgeoning area of large-sample nonparametric model fitting."
-Journal of the American Statistical Association
"Overall, my impressions of this book are very favorable, and I consider it a book worth owning... The material is presented clearly and attractively. I recommend it to anyone interested in or needing to use extensions to the general linear model."
"... a complete introduction to the topic in a single monograph... a very readable book that provides the reader with great insight into a vast array of data analysis techniques... an excellent text for a course on smoothing-based analysis techniques... highly recommended to any reader interested in an introduction to many of the recent developments in data analysis."