Smoothing and Regression: Approaches, Computation, and Application
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More About This Title Smoothing and Regression: Approaches, Computation, and Application
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MICHAEL G. SCHIMEK, PhD, 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.
English
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. Härdle & M. Müller).
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. Kötter).
Dynamic and Semiparametric Models (L. Fahrmeir & L. Knorr-Held).
Nonparametric Bayesian Bivariate Surface Estimation (M. Smith, et al.).
Index.
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. Härdle & M. Müller).
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. Kötter).
Dynamic and Semiparametric Models (L. Fahrmeir & L. Knorr-Held).
Nonparametric Bayesian Bivariate Surface Estimation (M. Smith, et al.).
Index.
English
From the publisher's description: "...a unique and important new resource destined to become on of the most frequently consulted references in the field." (Mathematical Reviews, 2001 f)
"...provides a comprehensive, concise coverage of statistics for engineers and scientists. I would recommend the use of this book for teaching statistics students..." (Journal of Quality Technology, Vol. 34, No. 1, January 2002)
"...provides a comprehensive, concise coverage of statistics for engineers and scientists. I would recommend the use of this book for teaching statistics students..." (Journal of Quality Technology, Vol. 34, No. 1, January 2002)
