.mopt_efficiency_const

Get the constant for the desired efficiency of the M-estimate of location
using the optimal \(\rho\) function

internal

Robust penalized (adaptive) elastic net S and M estimators for
linear regression. The adaptive methods are proposed in
Kepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the
non-adaptive methods in
Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
(2019) <doi:10.1214/19-AOAS1269>.
The package implements robust hyper-parameter selection with robust
information sharing cross-validation according to Kepplinger & Wei (2025)
<doi:10.1080/00401706.2025.2540970>.

David Kepplinger

pense

Penalized Elastic Net S/MM-Estimator of Regression

Matías Salibián-Barrera

Gabriela Cohen Freue

.mopt_efficiency_const function

<dl><dt>eff</dt>
<dd>desired efficiency (between 0 and 1)</dd>
<dt>eps</dt>
<dd>numerical tolerance for equality comparisons</dd></dl>

Arguments

Get the constant for the desired efficiency of the M-estimate of location
using the optimal \(\rho\) function — .mopt_efficiency_const

<dl>

<dt>eff</dt>
<dd>desired efficiency (between 0 and 1)</dd>


<dt>eps</dt>
<dd>numerical tolerance for equality comparisons</dd>

</dl>

.mopt_efficiency_const: Get the constant for the desired efficiency of the M-estimate of location using the optimal \(\rho\) function

Description

Usage

Value

Arguments