Non‐parametric regression with wavelet kernels

Alain Rakotomamonjy, Xavier Mary and Stéphane Canu

Applied Stochastic Models in Business and Industry, 2005, vol. 21, issue 2, 153-163

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.

Date: 2005
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/asmb.533

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:21:y:2005:i:2:p:153-163

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().