logLik.ebTobit

Marginal Log-likelihood of an ebTobit object

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

logLik.ebTobit function

<dl><dt>object</dt>
<dd>an object inheriting from class <code>ebTobit</code></dd>
<dt>...</dt>
<dd>not used</dd></dl>

Arguments

Marginal Log-likelihood of an ebTobit object — logLik.ebTobit

<dl>

<dt>object</dt>
<dd>an object inheriting from class <code>ebTobit</code></dd>


<dt>...</dt>
<dd>not used</dd>

</dl>

logLik.ebTobit: Marginal Log-likelihood of an ebTobit object

Description

Usage

Value

Arguments