Nonparametric Regression with Correlated Errors

Date
2000-05-01
Authors
Opsomer, Jean
Wang, Yuedong
Yang, Yuhong
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Altmetrics
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Statistics
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Statistics
Abstract

Nonparametric regression techniques are often sensitive to the presence of correlation in the errors. The practical consequences of this sensitivityare 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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This preprint was published as Jean Opsomer, Yuedong Wang and Yuhong Yang, "Nonparametric Regression with Correlated Errors", Statistical Science (2001), 134-153, doi: 10.1214/ss/1009213287.

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