Semiparametric Regression

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Cambridge University Press, Jul 14, 2003 - Mathematics - 386 pages
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Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The 2003 book is suitable as a textbook for students with little background in regression as well as a reference book for 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. Even experts on semiparametric regression should find something new here.
 

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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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