new_ebTobit

Validate the provided elements and populate the object. Current methods
require that <code>gr</code> is numeric for that calculation of posterior
statistics (mean and mediod).

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

new_ebTobit function

<dl><dt>prior</dt>
<dd>numeric vector of non-negative weights (sums to one)</dd>
<dt>gr</dt>
<dd>numeric matrix of support points</dd>
<dt>lik</dt>
<dd>numeric matrix of likelihoods</dd></dl>

Arguments

Create a new ebTobit object — new_ebTobit

<dl>

<dt>prior</dt>
<dd>numeric vector of non-negative weights (sums to one)</dd>


<dt>gr</dt>
<dd>numeric matrix of support points</dd>


<dt>lik</dt>
<dd>numeric matrix of likelihoods</dd>

</dl>

new_ebTobit: Create a new ebTobit object

Description

Usage

Value

Arguments