Wavelet thresholding for non-necessarily Gaussian noise: idealism

By By R. Averkamp, C. Houdr'e. 2003

54 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6b0668deca913699ebeadf25f3adf2836eaa3e0b

How researchers see this paper

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

In Analytical Chemistry

1. Tommaso Cai
2. Guy P. Nason
3. Maarten Jansen
4. Thomas C.M. Lee
5. D. Nychka
6. Hee‐Seok Oh
7. Harrison H. Zhou
8. Bernard Walter Silverman

In Computer Vision and Pattern Recognition

1. Guy P. Nason
2. Maarten Jansen
3. Lawrence D. Brown
4. Tommaso Cai
5. Thomas C.M. Lee
6. Hee‐Seok Oh
7. D. Nychka
8. Harrison H. Zhou

In Media Technology

1. Tommaso Cai

Citations in the corpus

• The Role of Pseudo Data for Robust Smoothing with Application to Wavelet Regression (2007, background): “Finally, Averkamp and Houdré (2003) extend the minimax theory for wavelet thresholding to some broader classes of known symmetric heavy tail noises.”
• Robust nonparametric estimation via wavelet median regression (2008, background): “Most closely related to our work is Averkamp and Houdré (2003, 2005) where the optimal minimax rate of global risk is studied.”
• Multiscale methods for data on graphs and irregular multidimensional situations (2009, background): “For example, see Johnstone and Silverman (1997) for correlated noise and Neumann and von Sachs (1995) and Averkamp and Houdré (2003) for non-Gaussian noise.”