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.
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