ConvexDual

This method only works if there is a working installation of <code>REBayes</code>
available. See the <code>REBayes</code> package and corresponding papers for more
implementation details.

Estimation tools for multidimensional Gaussian means using
empirical Bayesian g-modeling. Methods are able to handle fully observed data as
well as left-, right-, and interval-censored observations (Tobit
likelihood); descriptions of these methods can be found in Barbehenn and
Zhao (2023) <doi:10.48550/arXiv.2306.07239>. Additional, lower-level functionality based
on Kiefer and Wolfowitz (1956) <doi:10.1214/aoms/1177728066> and Jiang and
Zhang (2009) <doi:10.1214/08-AOS638> is provided that can be used to
accelerate many empirical Bayes and nonparametric maximum likelihood
problems.

Alton Barbehenn

ebTobit

Empirical Bayesian Tobit Matrix Estimation

Sihai Dave Zhao

ConvexDual function

<dl><dt>A</dt>
<dd>numeric matrix likelihoods</dd>
<dt>...</dt>
<dd>further arguments passed to <code>Rmosek</code> such as <code>rtol</code></dd></dl>

Arguments

Convex Optimization of the Kiefer-Wolfowitz NPMLE — ConvexDual

<dl>

<dt>A</dt>
<dd>numeric matrix likelihoods</dd>


<dt>...</dt>
<dd>further arguments passed to <code>Rmosek</code> such as <code>rtol</code></dd>

</dl>

ConvexDual: Convex Optimization of the Kiefer-Wolfowitz NPMLE

Description

Usage

Value

Arguments

Details