Description: 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

BundleDescription: Data sets from econometrics textbooks.
Packaged: Mon Feb 28 16:53:18 2005; yves

Contains: ETM Greene Hayashi StockWatson
Packaged: Fri Mar 18 09:27:21 2005; yves

Suggests: car, systemfit, sem, lmtest, sandwich
Description: Data sets from econometrics textbooks.
Packaged: Wed Apr 20 10:13:57 2005; yves

Packaged: Tue Oct 11 15:46:59 2005; yves

Packaged: Tue Oct 10 12:12:50 2006; yves

Suggests: car, systemfit, sem, lmtest, sandwich, gdata

Suggests: car, systemfit, sem, lmtest, sandwich, gdata, RCurl, XML, tis

Suggests: car, systemfit, sem, lmtest, sandwich, gdata, RCurl, XML,
Description: Data sets for econometrics.

Author: Yves Croissant <yves.croissant@let.ish-lyon.cnrs.fr> and Spencer Graves
Description: Data sets for econometrics, including political science.

Suggests: plm, systemfit, carData, wooldridge

Description: Functions to update datasets in Ecdat and to create,
manipulate, plot and analyze those and similar datasets.

Suggests: BMA, car, DescTools, Ecdat, maps, grid, gridBase, pryr
Imports: fda, gdata, RCurl, XML, tis, jpeg, MASS, TeachingDemos

Imports: fda, gdata, RCurl, XML, tis, jpeg, MASS, TeachingDemos,

Suggests: BMA, car, DescTools, Ecdat, maps, grid, gridBase, pryr, knitr
Description: Functions to update data sets in Ecdat and to create,
manipulate, plot and analyze those and similar data sets.