copula.multicorrelation.test

vector which indicates which columns are treated as one sample

these are passed to <code><a rd-options="" href="/link/cdm?package=multivariance&version=2.4.1" data-mini-rdoc="multivariance::cdm">cdm</a></code>

Formally it is nothing but tests for distance multivariance applied to the Monte Carlo emprical transform of the data. Hence its values vary for repeated runs.

Distance multivariance is a measure of dependence which can be used to detect
and quantify dependence of arbitrarily many random vectors. The necessary functions are
implemented in this packages and examples are given. It includes: distance multivariance,
distance multicorrelation, dependence structure detection, tests of independence and
copula versions of distance multivariance based on the Monte Carlo empirical transform.
Detailed references are given in the package description, as starting point for the
theoretic background we refer to:
B. B<c3><b6>ttcher, Dependence and Dependence Structures: Estimation and Visualization Using
the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020),
<doi:10.1515/stat-2020-0001>.

Bj<c3><b6>rn B<c3><b6>ttcher

multivariance

Measuring Multivariate Dependence Using Distance Multivariance

Martin Keller-Ressel

copula.multicorrelation.test function

these are passed to <code><a rd-options='' href='cdm'>cdm</a></code>

copula.multicorrelation.test: independence tests using the copula versions of distance multivariance

Description

Usage

Arguments

References