predict.smoothic

<code>predict</code> method class &#8220;<code>smoothic</code>&#8221;

Implementation of the SIC epsilon-telescope method, either
using single or distributional (multiparameter) regression. Includes classical regression
with normally distributed errors and robust regression, where the errors are from
the Laplace distribution. The "smooth generalized normal distribution" is used,
where the estimation of an additional shape parameter allows the user to move
smoothly between both types of regression. See O'Neill and Burke (2022)
"Robust Distributional Regression with Automatic Variable Selection" for more details.
<arXiv:2212.07317>. This package also contains the data analyses from O'Neill and
Burke (2023). "Variable selection using a smooth information criterion for distributional
regression models". <doi:10.1007/s11222-023-10204-8>.

Meadhbh O'Neill

smoothic

Variable Selection Using a Smooth Information Criterion

Kevin Burke

predict.smoothic function

<dl><dt>object</dt>
<dd>an object of class &#8220;<code>smoothic</code>&#8221; which is the result
of a call to <code>smoothic</code>.</dd>
<dt>newdata</dt>
<dd>new data object</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Predict smoothic — predict.smoothic

<dl>

<dt>object</dt>
<dd>an object of class &#8220;<code>smoothic</code>&#8221; which is the result
of a call to <code>smoothic</code>.</dd>


<dt>newdata</dt>
<dd>new data object</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

predict.smoothic: Predict smoothic

Description

Usage

Value

Arguments

Author

Examples