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.