consistency_const

efficiency_const

Returns the tuning constants required to achieve the desired
breakdown point or efficiency under the Normal model.

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

consistency_const function

<dl><dt>delta</dt>
<dd>desired breakdown point (between 0 and 0.5)</dd>
<dt>rho</dt>
<dd>the name of the chosen \(\rho\) function.
See <code>rho_function()</code> for a list of supported functions.</dd>
<dt>eps</dt>
<dd>numerical tolerance level for equality comparisons</dd>
<dt>eff</dt>
<dd>desired asymptotic efficiency (between 0.1 and 0.99).</dd></dl>

Arguments

Get the Constant for Consistency for the M-Scale and for Efficiency
for the M-estimate of Location — consistency_const

<dl>

<dt>delta</dt>
<dd>desired breakdown point (between 0 and 0.5)</dd>


<dt>rho</dt>
<dd>the name of the chosen \(\rho\) function.
See <code>rho_function()</code> for a list of supported functions.</dd>


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


<dt>eff</dt>
<dd>desired asymptotic efficiency (between 0.1 and 0.99).</dd>

</dl>

consistency_const: Get the Constant for Consistency for the M-Scale and for Efficiency for the M-estimate of Location

Description

Usage

Value

Arguments

See Also