Smoothing Techniques for Curve Estimation

Proceedings of a Workshop held in Heidelberg, April 2-4, 1979
Herausgegeben:Gasser, T.; Rosenblatt, M.

• Broschiertes Buch

Produktbeschreibung

Nonparametric curve estimation.- A tree-structured approach to nonparametric multiple regression.- Kernel estimation of regression functions.- Total least squares.- Some theoretical results on Tukey¿s 3R smoother.- Bias- and efficiency-robustness of general M-estimators for regression with random carriers.- Approximate conditional-mean type smoothers and interpolators.- Optimal convergence properties of kernel estimates of derivatives of a density function.- Density quantile estimation approach to statistical data modelling.- Global measures of deviation for kernel and nearest neighbor density estimates.- Some comments on the asymptotic behavior of robust smoothers.- Cross-validation techniques for smoothing spline functions in one or two dimensions.- Convergence rates of "thin plate" smoothing splines wihen the data are noisy.

Produktdetails

• Lecture Notes in Mathematics 757
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-09706-8
• 1979
• Seitenzahl: 256
• Erscheinungstermin: 1. November 1979
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 420g
• ISBN-13: 9783540097068
• ISBN-10: 3540097066
• Artikelnr.: 23571154

Inhaltsangabe

Nonparametric curve estimation.- A tree-structured approach to nonparametric multiple regression.- Kernel estimation of regression functions.- Total least squares.- Some theoretical results on Tukey's 3R smoother.- Bias- and efficiency-robustness of general M-estimators for regression with random carriers.- Approximate conditional-mean type smoothers and interpolators.- Optimal convergence properties of kernel estimates of derivatives of a density function.- Density quantile estimation approach to statistical data modelling.- Global measures of deviation for kernel and nearest neighbor density estimates.- Some comments on the asymptotic behavior of robust smoothers.- Cross-validation techniques for smoothing spline functions in one or two dimensions.- Convergence rates of "thin plate" smoothing splines wihen the data are noisy.