wfromt

Threshold value or vector of values.

A single value or a vector of standard deviations if the
 Laplace prior is used. If a vector, must have the same length as
 <code>tt</code>. Ignored if Cauchy prior is used.

Specification of prior to be used; can be
 <code>"cauchy"</code> or <code>"laplace"</code>.

prior

Scale factor if Laplace prior is used. Ignored if Cauchy
 prior is used.

Given a value or vector of thresholds and sampling standard deviations
(sd equals 1 for Cauchy prior), find the mixing weight for which this
is(these are) the threshold(s) of the posterior median estimator. If a
vector of threshold values is provided, the vector of corresponding
weights is returned.

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.

wfromt: Mixing weight from posterior median threshold

Description

Usage

Arguments

Value

References

See Also

Examples