sureshrink

SureShrink estimator of a high dimensional sparse parameter from Donoho and Johnstone (1995)

Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data.
For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020).

Trambak Banerjee

asus

Adaptive SURE Thresholding Using Side Information

Gourab Mukherjee

Wenguang Sun

sureshrink function

<dl><dt>d</dt>
<dd>an n vector of observations</dd>
<dt>v.d</dt>
<dd>an n vector of variances for each component of d</dd></dl>

Arguments

SureShrink estimator — sureshrink

<dl>

<dt>d</dt>
<dd>an n vector of observations</dd>


<dt>v.d</dt>
<dd>an n vector of variances for each component of d</dd>

</dl>

sureshrink: SureShrink estimator

Description

Usage

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Arguments

Details

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Examples