Nonparametric regression estimation using penalized least squares

IEEE Transactions on Information Theory

Volumn 47, Issue 7, 2001, Pages 3054-3058

  Kohler, Michael ^b   Krzyÿzak, Adam ^a,c  

^a IEEE   (Canada)

^b UNIVERSITY OF STUTTGART   (Germany)

^c Gina Cody School of Engineering and Computer Science   (Canada)

Author keywords

Penalized least squares; Regression estimate; Smoothing splines; Strong consistency; Vapnik Chervonenkis theory

Indexed keywords

REGRESSION ESTIMATION;

CONVERGENCE OF NUMERICAL METHODS; LEAST SQUARES APPROXIMATIONS; LINEAR EQUATIONS; POLYNOMIALS; REGRESSION ANALYSIS;

PARAMETER ESTIMATION;

EID: 0035504044     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/18.998089     Document Type: Article

References (26)

1. On the strong universal consistency of nearest neighbor regression function estimates
2. Distribution-free consistency results in nonparametric discrimination and regression function estimation
3. Interpolation des fonctions de deux variables suivant le principe de la flexion des plaques minces
4. Weak and strong universal consistency of semi-recursive partitioning and kernel regression estimates
5. On the strong universal consistency of a series type regression estimate
6. On the strong universal consistency of a recursive regression estimate by Pál Révész
7. On the universal consistency of a least squares spline regression estimator
8. Universally consistent regression function estimation using hierarchical B-splines
9. Nonparametric estimation via empirical risk minimization
10. Histogram regression estimation using data-dependent partitions
11. Consistent nonparametric regression
12. Optimal global rates of convergence for nonparametric regression
13. A new approach to least squares estimation, with applications
14. Estimating a regression function