Base-learner for linear quantile regression.

Boosting Regression Quantiles is a component-wise boosting algorithm,
that embeds all boosting steps in the well-established framework
of quantile regression. It is initialized with the corresponding quantile,
uses a quantile-specific learning rate, and uses quantile regression as its base learner.
The package implements this algorithm and allows cross-validation and stability selection.

Stefan Linner

boostrq

Boosting Regression Quantiles

brq function

<dl><dt>formula</dt>
<dd>a symbolic description of the base learner.</dd>
<dt>method</dt>
<dd>the algortihm used to fit the quantile regression, the default
is set to "fn", referring to the Frisch-Newton inferior point method.
For more details see the documentation of quantreg::rq.</dd></dl>

Arguments

base learner for boosting linear regression quantiles — brq

<dl>

<dt>formula</dt>
<dd>a symbolic description of the base learner.</dd>


<dt>method</dt>
<dd>the algortihm used to fit the quantile regression, the default
is set to "fn", referring to the Frisch-Newton inferior point method.
For more details see the documentation of quantreg::rq.</dd>

</dl>

brq: base learner for boosting linear regression quantiles

Description

Usage

Value

Arguments

Examples