predict.cv.hrq_glasso

Prediction for cv.hrq_glasso object

A program that conducts group variable selection for quantile and robust mean
regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for
group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss.
Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check
function can be approximated by the Huber loss for the median and the tilted version of
Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and
has also been shown to be statistical consistent.

Shaobo Li

hrqglas

Group Variable Selection for Quantile and Robust Mean Regression

Ben Sherwood

predict.cv.hrq_glasso function

<dl><dt>object</dt>
<dd>The model object of <code>cv.hrq_glasso</code>.</dd>
<dt>newX</dt>
<dd>New design matrix.</dd>
<dt>s</dt>
<dd>Value of lambda. If missing, the default is the <code>lambda.min</code>.</dd>
<dt>...</dt>
<dd>other input parameters.</dd></dl>

Arguments

Prediction for cv.hrq_glasso object — predict.cv.hrq_glasso

<dl>

<dt>object</dt>
<dd>The model object of <code>cv.hrq_glasso</code>.</dd>


<dt>newX</dt>
<dd>New design matrix.</dd>


<dt>s</dt>
<dd>Value of lambda. If missing, the default is the <code>lambda.min</code>.</dd>


<dt>...</dt>
<dd>other input parameters.</dd>

</dl>

predict.cv.hrq_glasso: Prediction for cv.hrq_glasso object

Description

Usage

Value

Arguments