Smoothing Techniques for Curve Estimation
Smoothing Techniques for Curve Estimation Proceedings of a Workshop held in Heidelberg, April 2–4, 1979 / [electronic resource] :
edited by Th. Gasser, M. Rosenblatt.
- 245 p. online resource.
- Lecture Notes in Mathematics, 757 0075-8434 ; .
- Lecture Notes in Mathematics, 757 .
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.
9783540384755
10.1007/BFb0098486 doi
Mathematics.
Mathematics, general.
QA1-939
510
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.
9783540384755
10.1007/BFb0098486 doi
Mathematics.
Mathematics, general.
QA1-939
510