Statistical Science

Nonparametric Regressin with Correlated Errors

Jean Opsomer, Yuedong Wang, and Yuhong Yang

Full-text: Open access

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.

Article information

Source
Statist. Sci., Volume 16, Issue 2 (2001), 134-153.

Dates
First available in Project Euclid: 24 December 2001

Permanent link to this document
https://projecteuclid.org/euclid.ss/1009213287

Digital Object Identifier
doi:10.1214/ss/1009213287

Mathematical Reviews number (MathSciNet)
MR1861070

Zentralblatt MATH identifier
1059.62537

Keywords
Kernel regression splines wavelet regression adaptive estimation smoothing parameter selection

Citation

Opsomer, Jean; Wang, Yuedong; Yang, Yuhong. Nonparametric Regressin with Correlated Errors. Statist. Sci. 16 (2001), no. 2, 134--153. doi:10.1214/ss/1009213287. https://projecteuclid.org/euclid.ss/1009213287


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