psi.huber

This function evaluates the first derivative of Huber's loss function.

Three robust marginal integration procedures for additive models based on local
polynomial kernel smoothers. As a preliminary estimator of the multivariate
function for the marginal integration procedure, a first approach uses local
constant M-estimators, a second one uses local polynomials of order 1 over all the
components of covariates, and the third one uses M-estimators based on local
polynomials but only in the direction of interest. For this last approach,
estimators of the derivatives of the additive functions can be obtained. All three
procedures can compute predictions for points outside the training set if desired.
See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.

Alejandra Martinez

rmargint

Robust Marginal Integration Procedures

Matias Salibian-Barrera

psi.huber function

<dl><dt>r</dt>
<dd>A vector of real numbers.</dd>
<dt>k</dt>
<dd>A positive tuning constant.</dd></dl>

Arguments

Matias Salibian-Barrera, <a href="/link/matias%40stat.ubc.ca?package=rmargint&version=2.0.3" data-mini-rdoc="rmargint::matias@stat.ubc.ca">matias@stat.ubc.ca</a>, Alejandra Martinez

Author

Derivative of Huber's loss function. — psi.huber

<dl>

<dt>r</dt>
<dd>A vector of real numbers.</dd>


<dt>k</dt>
<dd>A positive tuning constant.</dd>

</dl>

Matias Salibian-Barrera, <a href='mailto:matias@stat.ubc.ca'>matias@stat.ubc.ca</a>, Alejandra Martinez

psi.huber: Derivative of Huber's loss function.

Description

Usage

Value

Arguments

Author

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Examples