Smooth Design-Adapted Wavelets for Nonparametric Stochastic Regression
By Véronique Delouille, Jo Simoens, R. von Sachs. 2004
52 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7dad2d54cc4a3c81ca9178ed1b4fbc4ec23c3e7f
How researchers see this paper
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In Applied Mathematics
- Dominique Picard
- Guy P. Nason
- Maarten Jansen
- Gerard Kerkyacharian
- Bernard Walter Silverman
- Xiaohong Chen
- Demian Pouzo
- Lucien Birgé
In Computer Vision and Pattern Recognition
- Guy P. Nason
- Maarten Jansen
- Gerard Kerkyacharian
- Dominique Picard
- Bernard Walter Silverman
- Michael Elad
- Peter Hall
- Guillaume Lecué
In Statistics and Probability
- Maarten Jansen
- Dominique Picard
- Gerard Kerkyacharian
- Guy P. Nason
- Bernard Walter Silverman
- Jianqing Fan
- H. Muller
- Han Lin Shang
Citations in the corpus
- Regression in random design and warped wavelets (2004, methodology): “…refinement by Maxim (2002) for a random design; the weighted wavelet transform of Foster (1996); the isometric method of Sardy et al. (1999); the penalization method of Antoniadis and Fan (2001); and the specific construction of wavelets adapted to the design of Delouille et al. (2001; 2004).”
- Multiscale methods for data on graphs and irregular multidimensional situations (2009, methodology): “Adaptations of lifting to curve estimation problems in one dimension are discussed in Vanraes et al. (2002) and Delouille et al. (2004).”