Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

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. Contents......Page 3 Preface......Page 6 C01 - Introduction......Page 8 C02 - Smoothing......Page 12 C03 - Smoothing in Detail......Page 27 C04 - Additive Models......Page 49 C05 - Some Theory for Additive Models......Page 60 C06 - Generalized Additive Models......Page 76 C07 - Response Transformation Models......Page 95 C08 - Extensions to Other Settings......Page 108 C09 - Further Topics......Page 125 C10 - Case Studies......Page 148 App. A - Data......Page 158 App. B - Degrees of Freedom......Page 160 App. C - Software......Page 161 References......Page 163 Author Index......Page 170 Subject Index......Page 172 A variety of power tools for data analysis, based on non-parametric regression or smoothing techniques, are described in this text. These methods relax the usual linear assumption in many standard models, allowing the analyst to uncover structure in data Volume 43 of the series entitled, Monographs on Statistics and Applied Probability. Describes a new array of power tools for data analysis, based on nonparametric regression or smoothing techniques.