## Semiparametric RegressionScience abounds with problems where the data are noisy and the answer is not a straight line. Semiparametric regression aims to make sense of such data. Application areas include engineering, finance, medicine and public health. Semiparametric Regression Modeling explains this topic in a concise and modular fashion. The book is pitched towarards researchers and pro fessionals with little background in regression and statistically oriented scientists, such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. |

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Esse livro apresenta uma revisão de regressão paramétrica, regressão não parametrica, depois trabalha com a fusão das duas técnicas. O interessante seria fazer esta fusão, incorporando o questão dos dados autocorrelacionados.

### Contents

Parametric Regression | 15 |

Scatterplot Smoothing | 57 |

Mixed Models | 91 |

Automatic Scatterplot Smoothing | 112 |

Inference | 133 |

Simple Semiparametric Models | 161 |

Additive Models | 170 |

Semiparametric Mixed Models | 186 |

Measurement Error | 268 |

Bayesian Semiparametric Regression | 276 |

Spatially Adaptive Smoothing | 293 |

Analyses | 308 |

Epilogue | 320 |

Technical Complements | 326 |

A4 Probability Definitions and Results | 333 |

Computation of Covariance Matrix Estimators | 351 |

Generalized Parametric Regression | 194 |

Generalized Additive Models | 214 |

Interaction Models | 223 |

Bivariate Smoothing | 238 |

Variance Function Estimation | 261 |

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### Common terms and phrases

additive model amount of smoothing analysis approximate autocorrelation Bayesian bias birthweight bivariate BLUP Chapter coefficients computed confidence bands confidence intervals corresponding covariance matrix cubic curve data set degrees of freedom density derivative diagonal discussed distribution eigenvector example fitted values fixed effects given global penalty homoscedasticity inference interaction iterations least squares LIDAR LIDAR data likelihood ratio linear mixed model linear model linear regression linear spline logistic logkwh maximum likelihood MCMC mean measurement error methods minimizes model selection Monte Carlo nonlinear nonparametric regression number of knots p-value parametric regression penalized spline penalty estimate plot pointwise polynomial prediction predictor variables problem random effects regression function regression model REML residuals response Ruppert S-PLUS salinity sample scatterplot scatterplot smooth semiparametric models semiparametric regression shows simulation smoothers smoothing spline spatially spline fit spline model statistical studentized residuals temperature tion variance components variance function vector xgrid zero

### Popular passages

Page 374 - Wood, SN (2000). Modelling and smoothing parameter estimation with multiple quadratic penalties.

Page 374 - A generalized approximate cross validation for smoothing splines with non-Gaussian data, Statistica Sinica 6: 675-92, What explains complexity?

Page 362 - Cai, Z., Fan, J., and Li, R., 2000. Efficient estimation and inferences for varying-coefficient models. Journal of the American Statistical Association 95, 888-902.