CKT.predict.nNets

Predict the values of conditional Kendall's tau
using Model Averaging of Neural Networks

Provides functions for the estimation of conditional copulas models,
various estimators of conditional Kendall's tau
(proposed in Derumigny and Fermanian (2019a, 2019b, 2020)
<doi:10.1515/demo-2019-0016>,
<doi:10.1016/j.csda.2019.01.013>,
<doi:10.1016/j.jmva.2020.104610>),
test procedures for the simplifying assumption
(proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011>
and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>),
and measures of non-simplifyingness
(proposed in Derumigny (2025) <doi:10.48550/arXiv.2504.07704>).

Alexis Derumigny

CondCopulas

Estimation and Inference for Conditional Copula Models

Jean-David Fermanian

Aleksey Min

Rutger van der Spek

CKT.predict.nNets function

<dl><dt>fit</dt>
<dd>result of a call to <code>CKT.fit.nNet</code></dd>
<dt>newZ</dt>
<dd>new matrix of observations, with the same number of variables.
and same names as the <code>designMatrix</code> that was used to fit the neural networks.</dd>
<dt>aggregationMethod</dt>
<dd>the method to be used to aggregate all the predictions
together. Can be <code>"mean"</code> or <code>"median"</code>.</dd></dl>

Arguments

Predict the values of conditional Kendall's tau
using Model Averaging of Neural Networks — CKT.predict.nNets

<dl>

<dt>fit</dt>
<dd>result of a call to <code>CKT.fit.nNet</code></dd>


<dt>newZ</dt>
<dd>new matrix of observations, with the same number of variables.
and same names as the <code>designMatrix</code> that was used to fit the neural networks.</dd>


<dt>aggregationMethod</dt>
<dd>the method to be used to aggregate all the predictions
together. Can be <code>"mean"</code> or <code>"median"</code>.</dd>

</dl>

CKT.predict.nNets: Predict the values of conditional Kendall's tau using Model Averaging of Neural Networks

Description

Usage

Value

Arguments