## 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

additive model alternative analysis applied approach appropriate approximation assume asymptotic average backfitting algorithm basis Chapter components computed conditional consider constant convergence correlation corresponding criterion cubic curves defined degrees of freedom denote dependent derived described deviance diagonal discussed distribution effect eigenvalues equivalent error estimate example Exercise expected Figure functions give given hence important interaction iterative knots least-squares likelihood linear model linear regression matrix mean method minimize natural nonparametric Note observations obtain operator optimal parameter penalized plot points prediction predictor probability problem procedure produces projection region regression represent residual response sample scoring seasonal selection similar simple single smoother smoothing parameter smoothing splines solution solve space squared standard Statist step Suppose surface symmetric transformation usual values variable variance vector weights zero