## Generalized Additive ModelsThis 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

Smoothing | 6 |

Additive models | 82 |

Some theory for additive models | 105 |

Generalized additive models | 136 |

Response transformation models | 174 |

Extensions to other settings | 201 |

Further topics | 235 |

Smoothing in detail | 242 |

Case studies | 281 |

Appendices | 301 |

References | 311 |

325 | |

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### Common terms and phrases

ACE algorithm additive fit additive model adjusted dependent variable analysis approximation asymptotic B-splines backfitting algorithm binary Breiman Buja canonical correlation Chapter components computed concurvity convergence correlation covariance criterion cross-validation cubic smoothing spline cubic spline curves defined degrees of freedom denote derived described deviance df err diagonal discussed distribution eigenvalues eigenvectors error estimating equations example Exercise exponential family fitted functions fitted values Gaussian interaction iterative kernel smoothers Kullback-Leibler distance kyphosis least-squares likelihood linear model linear regression linear smoothers link function log-likelihood logistic logistic regression method minimize neighbourhood Newton-Raphson nonlinear nonparametric observations operator optimal orthogonal ozone partial residuals penalized least-squares plot points pointwise predictor problem proportional-hazards model regression model regression splines running-line smoother sample scatterplot scatterplot smoother semi-parametric simple smoother matrix smoothing parameter solution squared Statist surface symmetric tr(S variance vector virus level Wahba weights zero