huberloss

Several robust estimators for linear regression and variable selection are provided.
Included are Maximum tangent likelihood estimator by Qin, et al., (2017), arXiv preprint <doi:10.48550/arXiv.1708.05439>,
least absolute deviance estimator and Huber regression. The penalized version of each of these
estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to
produce consistent estimates for both fixed and high-dimensional settings.

Shaobo Li

Maximum Tangent Likelihood Estimation for Robust Linear
Regression and Variable Selection

Yichen Qin

huberloss function

<dl><dt>r</dt>
<dd>residual, y-Xbeta</dd>
<dt>alpha</dt>
<dd>1/alpha is the huber tuning parameter delta. Larger alpha results in smaller portion of squared loss.</dd></dl>

Arguments

Huber Loss — huberloss

<dl>

<dt>r</dt>
<dd>residual, y-Xbeta</dd>


<dt>alpha</dt>
<dd>1/alpha is the huber tuning parameter delta. Larger alpha results in smaller portion of squared loss.</dd>

</dl>

huberloss: Huber Loss

Description

Usage

Value

Arguments