Threshold selection for wavelet shrinkage of noisy data

Methods based on thresholding and shrinking empirical wavelet coefficients hold promise for recovering and/or denoising signals observed in noise. Here the authors review and compare various proposals for the choice of thresholds. These include soft and hard thresholding, and thresholds that are fixed in advance or chosen level by level from an empirical optimality criterion. The authors present results from simulations and real data examples.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>