Wavelet Deconvolution in a Periodic Setting with Long-Range Dependent Errors

Abstract:

A hard threshold wavelet estimator is constructed for a deconvolution model in a periodic setting that has long-range dependent noise. The estimation paradigm is based on a maxiset method that attains a near optimal rate of convergence for a variety of L_p loss functions and a wide variety of Besov spaces in the presence of strong dependence. The effect of long-range dependence is detrimental to the rate of convergence. The method is implemented using a modification of the WaveD-package in R and an extensive numerical study is conducted. The study supplements the theoretical results and compares the LRD estimator with naively using the standard WaveD approach.