DocumentCode
1047995
Title
Quantization for Nonparametric Regression
Author
Györfi, László ; Wegkamp, Marten
Author_Institution
Budapest Univ. of Technol. & Econ., Budapest
Volume
54
Issue
2
fYear
2008
Firstpage
867
Lastpage
874
Abstract
The authors discuss quantization or clustering of nonparametric regression estimates. The main tools developed are oracle inequalities for the rate of convergence of constrained least squares estimates. These inequalities yield fast rates for both nonparametric (unconstrained) least squares regression and clustering of partition regression estimates and plug-in empirical quantizers. The bounds on the rate of convergence generalize known results for bounded errors to subGaussian, too.
Keywords
convergence; least mean squares methods; nonparametric statistics; quantisation (signal); regression analysis; bounded error; convergence; mean squared error method; nonparametric least squares regression; vector quantization; Additive noise; Convergence; Data compression; Least squares approximation; Multivariate regression; Probability distribution; Temperature; Vector quantization; Weather forecasting; Yield estimation; Regression estimation with restriction; finite-sample bounds; least squares estimates; vector quantization;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.2007.913565
Filename
4439850
Link To Document