Wavelet Block Thresholding for Non-Gaussian Errors

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. In this paper, a distribution-free method for thresholding wavelet coefficients in nonparametric regression is described. Unlike some other non-normal error thresholding methods, the proposed method does not assume the form of the nonnormal distribution is known. We improve upon 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.