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.


Dr Justin Wishart

Research Area

School of Mathematics and Statistics, UNSW


Fri, 03/08/2012 - 4:00pm to 5:00pm


OMB-145, Old Main Building, UNSW Kensington Campus