A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression
Smoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis.
Using examples of applications from the biosciences, environmental sciences, engineering, and economics, as well as medical research and marketing, this volume addresses the theory, computation, and application of each approach. A number of the techniques discussed, such as smoothing under shape restrictions or of dependent data, are presented for the first time in book form. Special features of this book include:
* Comprehensive coverage of smoothing and regression with software hints and applications from a wide variety of disciplines
* A unified, easy-to-follow format
* Contributions from more than 25 leading researchers from around the world
* More than 150 illustrations also covering new graphical techniques important for exploratory data analysis and visualization of high-dimensional problems
* Extensive end-of-chapter references
For professionals and aspiring professionals in statistics, applied mathematics, computer science, and econometrics, as well as for researchers in the applied and social sciences, Smoothing and Regression is a unique and important new resource destined to become one the most frequently consulted references in the field.
Tabela de Conteúdo
Spline Regression (R. Eubank).
Variance Estimation and Smoothing-Parameter Selection for Spline Regression (A. van der Linde).
Kernel Regression (P. Sarda & P. Vieu).
Variance Estimation and Bandwidth Selection for Kernel Regression (E. Herrmann).
Spline and Kernel Regression under Shape Restrictions (M. Delecroix & C. Thomas-Agnan).
Spline and Kernel Regression for Dependent Data (R. Kohn, et al.).
Wavelets for Regression and Other Statistical Problems (G. Nason & B. Silverman).
Smoothing Methods for Discrete Data (J. Simonoff & G. Tutz).
Local Polynomial Fitting (J. Fan & I. Gijbels).
Additive and Generalized Additive Models (M. Schimek & B. Turlach).
Multivariate Spline Regression (C. Gu).
Multivariate and Semiparametric Kernel Regression (W. Hardle & M. Muller).
Spatial-Process Estimates as Smoothers (D. Nychka).
Resampling Methods for Nonparametric Regression (E. Mammen).
Multidimensional Smoothing and Visualization (D. Scott).
Projection Pursuit Regression (S. Klinke & J. Grassmann).
Sliced Inverse Regression (T. Kotter).
Dynamic and Semiparametric Models (L. Fahrmeir & L. Knorr-Held).
Nonparametric Bayesian Bivariate Surface Estimation (M. Smith, et al.).
Index.
Sobre o autor
MICHAEL G. SCHIMEK, Ph D, DPhil, is Professor of Statistics and Biometrics in the Department of Medical Informatics, Statistics, and Documentation at Karl-Franzens-University of Graz, Austria, and Adjunct Professor of Methodology in the Department of Psychology at the University of Vienna, Austria.
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