Semiparametric Regression

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Cambridge University Press, Jul 14, 2003 - Mathematics - 386 pages
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Science 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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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.

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