HermiteCov12

Computation of the covariance matrix between d-variate T-Hermite polynomials
\(H_N(X_1)\) and \(H_N(X_2)\).

Algorithms to build set partitions and commutator matrices and their use in the
construction of multivariate d-Hermite polynomials;  estimation and derivation of theoretical  vector moments and vector
cumulants  of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to
estimation and derivation of multivariate measures of skewness and kurtosis;  estimation and derivation of asymptotic
covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis.
The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".

Emanuele Taufer

MultiStatM

Multivariate Statistical Methods

Gyorgy Terdik

HermiteCov12 function

<dl><dt>SigX12</dt>
<dd>Covariance matrix of the Gaussian vectors X1 and X2 respectively
of dimensions d1 and d2</dd>
<dt>N</dt>
<dd>Common degree of the multivariate Hermite polynomials</dd></dl>

Arguments

Covariance matrix  for multivariate  T-Hermite polynomials — HermiteCov12

<dl>

<dt>SigX12</dt>
<dd>Covariance matrix of the Gaussian vectors X1 and X2 respectively
of dimensions d1 and d2</dd>


<dt>N</dt>
<dd>Common degree of the multivariate Hermite polynomials</dd>

</dl>

HermiteCov12: Covariance matrix for multivariate T-Hermite polynomials

Description

Usage

Value

Arguments

References

See Also

Examples