Smoothing and Regression: Approaches, Computation, and Application

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. Hrdle & M. Mller). 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. Ktter). Dynamic and Semiparametric Models (L. Fahrmeir & L. Knorr-Held). Nonparametric Bayesian Bivariate Surface Estimation (M. Smith, et al.). Index.