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Non‐parametric regression with wavelet kernels

Non‐parametric regression with wavelet kernels This paper introduces a method to construct a reproducing wavelet kernel Hilbert spaces for non‐parametric regression estimation when the sampling points are not equally spaced. Another objective is to make high‐dimensional wavelet estimation problems tractable. It then provides a theoretical foundation to build reproducing kernel from operators and a practical technique to obtain reproducing kernel Hilbert spaces spanned by a set of wavelets. A multiscale approximation technique that aims at taking advantage of the multiresolution structure of wavelets is also described. Examples on toy regression and a real‐world problem illustrate the effectiveness of these wavelet kernels. Copyright © 2005 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Non‐parametric regression with wavelet kernels

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References (22)

Publisher
Wiley
Copyright
Copyright © 2005 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.533
Publisher site
See Article on Publisher Site

Abstract

This paper introduces a method to construct a reproducing wavelet kernel Hilbert spaces for non‐parametric regression estimation when the sampling points are not equally spaced. Another objective is to make high‐dimensional wavelet estimation problems tractable. It then provides a theoretical foundation to build reproducing kernel from operators and a practical technique to obtain reproducing kernel Hilbert spaces spanned by a set of wavelets. A multiscale approximation technique that aims at taking advantage of the multiresolution structure of wavelets is also described. Examples on toy regression and a real‐world problem illustrate the effectiveness of these wavelet kernels. Copyright © 2005 John Wiley & Sons, Ltd.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Mar 1, 2005

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