Wavelet Shrinkage: Asymptopia?

By David L. Donoho, Iain M. Johnstone, Gerard Kerkyacharian, Dominique Picard. 1995

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Citations in the corpus

PREDICTIVE LEARNING VIA RULE ENSEMBLES (2008, background): “M 1 , often with many being set to zero [see Tibshirani (1996), Donoho et al. (1995)].”
Adapting to unknown sparsity by controlling the false discovery rate (2005, background): “(1.2) A compelling motivation for this strategy is provided by wavelet analysis, since the wavelet representation of many smooth and piecewise smooth signals is sparse in precisely our sense (Donoho et al., 1995).”
Variable Selection in Predictive Regressions (2013, background): “As noted in Eickmeier and Ziegler (2008), there is much heterogeneity in empirical work about the choice of both parameters.”
Why Simple Shrinkage Is Still Relevant for Redundant Representations? (2006, background): “In a sequence of publications they advocated the use of sparsity of the wavelet coefficients as a driving force in recovering the desired signal [1, 2, 3, 4, 5, 6].”
Signal Recovery by Proximal Forward-Backward Splitting (2005, methodology): “Then, it follows from Example 2.20 that z ⊕ is the wavelet soft-thresholded transformation of z [33, 34].”
Posterior concentration rates for infinite dimensional exponential families (2012, unknown): “[7] or Reynaud-Bouret et al.”
The Operating Point of the Cortex: Neurons as Large Deviation Detectors (2007, methodology): “…of the generator potential may serve to generate a de-noised version of the retinal image by a procedure analogous to “wavelet shrinkage” (Donoho and Johnstone, 1994, 1995; Donoho et al., 1995; Chang et al., 2000a,b; Jung and Scharcanski, 2003; Bacchelli and Papi, 2004; Abramovich et al., 2006).”
Bayesian Wavelet Regression on Curves With Application to a Spectroscopic Calibration Problem (2001, methodology): “Mixture priors have been widely used for variable selection in the original model space, originally by Leamer (1978) and more recently by George and McCulloch (1997) and Mitchell and Beauchamp (1988) for the linear multiple regression case.”
BAMS Method: Theory and Simulations (2001, background): “…tionWavelet based statisti al shrinkage pro edures proposed by Donoho, John-stone, and their o-authors in the early 1990's (Donoho and Johnstone (1994),Donoho et al. (1995), and referen es in Donoho (1997)) have proven to bean ex ellent data analyti tool in fun tion/density estimation problems.”
Wavelets in Economics and Finance: Past and Future (2002, background): “The implications for insight into the analysis of non-homogeneous signals is very important as the graphs presented by Donoho et al (1995) demonstrate.”
AMlet, RAMlet, and GAMlet: Automatic Nonlinear Fitting of Additive Models, Robust and Generalized, With Wavelets (2004, background): “Donoho et al. (1995) proved that, with the universal smoothing parameter ¶ = ¼ p 2 log N , Waveshrink is nearly minimax for a wide variety of loss functions and for a wide range of smoothness classes.”