Nonparametric Regression with Correlated Errors

Jean Opsomer, Yuedong Wang, Yuhong Yang

Research output: Contribution to journalArticle

162 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)134-153
Number of pages20
JournalStatistical Science
Volume16
Issue number2
DOIs
StatePublished - 2001

Keywords

  • Adaptive estimation
  • Kernel regression
  • Smoothing parameter selection
  • Splines
  • Wavelet regression

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