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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

Abstract

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. https://doi.org/10.1214/ss/1009213287

Information

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

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

Rights: Copyright © 2001 Institute of Mathematical Statistics

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Vol.16 • No. 2 • May 2001
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