Smoothing and Regression: Approaches, Computation, and Application
Buy Rights Online Buy Rights

Rights Contact Login For More Details

More About This Title Smoothing and Regression: Approaches, Computation, and Application


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.


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.).



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)