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The wavelet detection of the jump and cusp points of a regression function

The wavelet detection of the jump and cusp points of a regression function Wavelets are applied to a regression model with an additive stationary noise. By checking the empirical wavelet coefficients with significantly large absolute values across fine scale levels, the jump points are detected first. Then the cusp points are identified by checking the wavelet coefficients with significantly large absolute values which are secondly, large only to the previous wavelet coefficient across fine scale levels. All estimators are shown to be consistent. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

The wavelet detection of the jump and cusp points of a regression function

Acta Mathematicae Applicatae Sinica , Volume 16 (3) – Jul 18, 2007

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References (13)

Publisher
Springer Journals
Copyright
Copyright © 2000 by Science Press
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/BF02679893
Publisher site
See Article on Publisher Site

Abstract

Wavelets are applied to a regression model with an additive stationary noise. By checking the empirical wavelet coefficients with significantly large absolute values across fine scale levels, the jump points are detected first. Then the cusp points are identified by checking the wavelet coefficients with significantly large absolute values which are secondly, large only to the previous wavelet coefficient across fine scale levels. All estimators are shown to be consistent.

Journal

Acta Mathematicae Applicatae SinicaSpringer Journals

Published: Jul 18, 2007

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