Open Access
May 2001 Nonparametric Regressin with Correlated Errors
Jean Opsomer, Yuedong Wang, Yuhong Yang
Statist. Sci. 16(2): 134-153 (May 2001). DOI: 10.1214/ss/1009213287


Nonparametric regression techniques are often sensitive to the presence of correlation in the errors. The practical consequences of this sensitivity are explained, including the breakdown of several popular data-driven smoothing parameter selection methods. We review the existing literature in kernel regression, smoothing splines and wavelet regression under correlation, both for short-range and long-range dependence. Extensions to random design, higher dimensional models and adaptive estimation are discussed.


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Jean Opsomer. Yuedong Wang. Yuhong Yang. "Nonparametric Regressin with Correlated Errors." Statist. Sci. 16 (2) 134 - 153, May 2001.


Published: May 2001
First available in Project Euclid: 24 December 2001

zbMATH: 1059.62537
MathSciNet: MR1861070
Digital Object Identifier: 10.1214/ss/1009213287

Keywords: adaptive estimation , kernel regression , smoothing parameter selection , splines , wavelet regression

Rights: Copyright © 2001 Institute of Mathematical Statistics

Vol.16 • No. 2 • May 2001
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