  Wavelet thresholding generally assumes independent, identically distributed
normal errors when estimating functions in a nonparametric regression setting.
VisuShrink and SureShrink are just two of the many common thresholding methods
based on this assumption. When the errors are not normally distributed,
however, few methods have been proposed. A distribution-free method for
thresholding wavelet coefficients in nonparametric regression is described,
which unlike some other non-normal error thresholding methods, does not assume
the form of the non-normal distribution is known. Improvements are made to an
existing even-odd cross-validation method by employing block thresholding and
level dependence. The efficiency of the proposed method on a variety of
non-normal errors, including comparisons to existing wavelet threshold
estimators, is shown on both simulated and real data.
