Nonparametric Regression with Correlated Errors
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.
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.