View source: R/Approximations.r
| GramCharlier | R Documentation |
Provides the truncated Gram-Charlier approximation to a multivariate density. Approximation can be up to the first k=8 cumulants according to the formula
f_{\mathbf{X}}\left( \mathbf{x}\right) =\left(
1+\sum_{k=3}^{\infty }\frac{1}{k!}\mathbf{B}_{k}^{\intercal }\left( 0,0,
\boldsymbol{\kappa }_{\mathbf{Y},3}^{\otimes },\ldots \boldsymbol{\kappa }_{
\mathbf{Y},k}^{\otimes }\right) \mathbf{H}_{k}\left( \mathbf{y}|\mathbf{I}
\right) \right) \varphi \left( \mathbf{x}|\boldsymbol{\mu},\boldsymbol{\Sigma }_{
\mathbf{X}}\right),
where the Hermite polynomial \mathbf{H}_{k}\left( \mathbf{y}| \mathbf{I}\right)
=\mathbf{H}_{k}\left( \boldsymbol{\Sigma }^{-1/2}\mathbf{x} \right) corresponds to the standard Gaussian variate,
the cumulants are the cumulants of the standardized variate \mathbf{Y}=
\boldsymbol{\Sigma }^{-1/2}\left( \mathbf{X}-\boldsymbol{\mu }\right) of
\mathbf{X}, (\boldsymbol{\mu }={E}\mathbf{X}) and \varphi \left(
\mathbf{x}\boldsymbol{|\boldsymbol{\mu},\Sigma }\right) denotes the multivariate normal
density function with mean \boldsymbol{\mu} and variance matrix \boldsymbol{
\Sigma }.
GramCharlier(X, cum)
X |
A matrix of d-variate data |
cum |
a list containing the raw (unstandardized) cumulant vectors of X. At least the first 3 cumulants need to be provided |
The vector of the Gram-Charlier density evaluated at X
Gy.Terdik, Multivariate statistical methods - Going beyond the linear, Springer 2021. Section 4.7.
Other Approximations:
Edgeworth(),
IntEdgeworth(),
IntGramCharlier(),
MTCE()
# Gram-Charlier density approximation (k=4) of data generated from
# a bivariate skew-gaussian distribution
n<-500
alpha<-c(10,0)
omega<-diag(2)
X<-rSkewNorm(n,omega,alpha)
EC<-SampleMomCum(X,r=4,centering=FALSE,scaling=FALSE)
EC<-EC$estCum.r ## (estimated) raw cumulants of X
fx4<-GramCharlier(X[1:50,],cum=EC)
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