Empirical Bayes thresholding : adapting to sparsity when it advantageous to do so

@inproceedings{Silverman2006EmpiricalBT,
  title={Empirical Bayes thresholding : adapting to sparsity when it advantageous to do so},
  author={B. W. Silverman},
  year={2006}
}
Suppose one is trying to estimate a high dimensional vector of parameters from a series of one observation per parameter. Often, it is possible to take advantage of sparsity in the parameters by thresholding the data in an appropriate way. A marginal maximum likelihood approach, within a suitable Bayesian structure, has excellent properties. For very sparse signals, the procedure chooses a large threshold and takes advantage of the sparsity, while for signals where there are many non-zero… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…