threshld

<p>specifies whether hard or soft thresholding is applied</p>

hard

<p>Given a data value or a vector of data, threshold the data at a
  specified value, using hard or soft thresholding.</p>

nonparametric

Empirical Bayes thresholding using the methods developed
by I. M. Johnstone and B. W. Silverman. The basic problem is to
estimate a mean vector given a vector of observations of the mean
vector plus white noise, taking advantage of possible sparsity in
the mean vector. Within a Bayesian formulation, the elements of
the mean vector are modelled as having, independently, a
distribution that is a mixture of an atom of probability at zero
and a suitable heavy-tailed distribution. The mixing parameter can
be estimated by a marginal maximum likelihood approach. This leads
to an adaptive thresholding approach on the original data.
Extensions of the basic method, in particular to wavelet
thresholding, are also implemented within the package.

Peter Carbonetto

EbayesThresh

Empirical Bayes Thresholding and Related Methods

Bernard W. Silverman

Ludger Evers

Kan Xu

Matthew Stephens

threshld function

Threshold data with hard or soft thresholding — threshld

Threshold data with hard or soft thresholding

threshld: Threshold data with hard or soft thresholding

Description

Usage

Arguments

Value

References

See Also

Examples