huber.reg

This function produces Huber estimates for linear regression. Initial estimates is required.
Currently, the function does not support automatic selection of huber tuning parameter.

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

huber.reg function

<dl><dt>y</dt>
<dd>the response vector</dd>
<dt>X</dt>
<dd>design matrix</dd>
<dt>beta.ini</dt>
<dd>initial value of estimates, could be from OLS.</dd>
<dt>alpha</dt>
<dd>1/alpha is the huber tuning parameter delta. Larger alpha results in smaller portion of squared loss.</dd>
<dt>intercept</dt>
<dd>logical input that indicates if intercept needs to be estimated. Default is FALSE.</dd></dl>

Arguments

Huber estimation for linear regression — huber.reg

<dl>

<dt>y</dt>
<dd>the response vector</dd>


<dt>X</dt>
<dd>design matrix</dd>


<dt>beta.ini</dt>
<dd>initial value of estimates, could be from OLS.</dd>


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


<dt>intercept</dt>
<dd>logical input that indicates if intercept needs to be estimated. Default is FALSE.</dd>

</dl>

huber.reg: Huber estimation for linear regression

Description

Usage

Value

Arguments

Examples