AIC.ldt.estim

This function extracts Akaike information criterion from an <code>ldt.estim</code> object.

Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others).
'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented.
Evaluations are both in-sample and out-of-sample.
It is designed to be efficient in terms of CPU usage and memory consumption.

Ramin Mojab

Automated Uncertainty Analysis

Stephen Becker

AIC.ldt.estim function

<dl><dt>object</dt>
<dd>An object of class <code>ldt.estim</code></dd>
<dt>...</dt>
<dd>Additional arguments.</dd>
<dt>k</dt>
<dd>Unused parameter.</dd></dl>

Arguments

Akaike Information Criterion — AIC.ldt.estim

<dl>

<dt>object</dt>
<dd>An object of class <code>ldt.estim</code></dd>


<dt>...</dt>
<dd>Additional arguments.</dd>


<dt>k</dt>
<dd>Unused parameter.</dd>

</dl>

AIC.ldt.estim: Akaike Information Criterion

Description

Usage

Value

Arguments