posterior_L1mediod.ebTobit

The posterior L1 mediod is defined as \arg\min_y E |y - t|_1 where the
expectation is taken over the posterior t|X=x. Here the posterior L1 mediod
is evaluated for each of the observations used to fit <code>object</code>.

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

posterior_L1mediod.ebTobit function

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

Arguments

Compute the Posterior L1 Mediod of an ebTobit object — posterior_L1mediod.ebTobit

<dl>

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

</dl>

posterior_L1mediod.ebTobit: Compute the Posterior L1 Mediod of an ebTobit object

Description

Usage

Value

Arguments