Least angle regression
The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to whi...
Publications
7 shownIdeal Spatial Adaptation by Wavelet Shrinkage
Journal Article Ideal spatial adaptation by wavelet shrinkage Get access David L Donoho, David L Donoho Department of Statistics, Stanford University, Stanford, California, U.S....
Adapting to Unknown Smoothness via Wavelet Shrinkage
Abstract We attempt to recover a function of unknown smoothness from noisy sampled data. We introduce a procedure, SureShrink, that suppresses noise by thresholding the empirica...
Minimax estimation via wavelet shrinkage
We attempt to recover an unknown function from noisy, sampled data.\nUsing orthonormal bases of compactly supported wavelets, we develop a nonlinear\nmethod which works in the w...
Empirical Functionals and Efficient Smoothing Parameter Selection
SUMMARY A striking feature of curve estimation is that the smoothing parameter ĥ 0, which minimizes the squared error of a kernel or smoothing spline estimator, is very difficul...
Ideal spatial adaptation by wavelet shrinkage
SUMMARY With ideal spatial adaptation, an oracle furnishes information about how best to adapt a spatially variable estimator, whether piecewise constant, piecewise polynomial, ...
Adapting to unknown sparsity by controlling the false discovery rate
We attempt to recover an n-dimensional vector observed in white noise, where n is large and the vector is known to be sparse, but the degree of sparsity is unknown. We consider ...
Frequent Co-Authors
Researcher Info
- h-index
- 7
- Publications
- 7
- Citations
- 22,677
- Institution
- Stanford University
External Links
Identifiers
- ORCID
- 0000-0002-2865-3076
Impact Metrics
h-index
7
h-index: Number of publications with at least h citations each.