Empirical Bayes approach to block wavelet function estimation

62 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting (2008, background): “According to [ 3 ], wavelet not only decompose the data in terms of times and frequency, but also can reduce lots of processing times.”
Image denoising using scale mixtures of Gaussians in the wavelet domain (2003, background): “Specifically, a number of authors have estimated the local variance from a collection of wavelet coefficients at nearby positions, scales, and/or orientations, and then used these estimated variances in order to denoise the coefficients [16], [21], [28], [31]–[33].”
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study (2001, methodology): “Therefore, in our simulation study in Section 5, we only consider translation invariant thresholding in conjunction with the above threshold and with both hard thresholding (4) and soft thresholding (5).”
Regularization of Wavelet Approximations (2001, background): “Abramovich, Besbeas, and Sapatinas (2000) considered Bayesian wavelet block shrinkage and blockwise thresholding estimators and indicated that they outperform existing classical blockwise thresholding estimators in terms of mean squared error via simulation studies.”
Real nonparametric regression using complex wavelets (2004, unknown): “thresholding procedure from Abramovich et al. (2002) , the EBayesthresh rule of Johns-”