EbayesThresh

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.

Last 30 days

This package has been downloaded 78,159 times in the last 30 days. The kind of number that gets mentioned in a keynote speech. Well done! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2,611 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 650,556 times in the last 365 days. Half a million downloads! This work is now a household name in certain academic circles. The day with the most downloads was Jan 03, 2025 with 2,685 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.