View source: R/Approximations.r
| Edgeworth | R Documentation |
Provides the truncated Edgeworth approximation to a multivariate density of
W = \sqrt{n} \bar{X}
Approximation can use up to the first k=8 cumulants. The function implements the formula
f_{\mathbf{W}^{\left( n\right) }}\left( \mathbf{w}\right) =\left(
1+\sum_{k=1}^{\infty }\frac{n^{-k/2}}{k!}\mathbf{B}_{k}\left( \frac{%
\boldsymbol{\kappa }_{\mathbf{Y},3}^{\otimes \intercal }\mathbf{H}_{3}\left(
\mathbf{z}|\mathbf{I}\right) }{6},\ldots ,\frac{
\boldsymbol{\kappa }_{\mathbf{Y},k+2}^{\otimes }\mathbf{H}_{k+2}\left(
\mathbf{z}|\mathbf{I}\right) }{\left( k+1\right) \left( k+2\right) }\right)
\right) \varphi \left( \mathbf{w}|\boldsymbol{\Sigma }_{\mathbf{X}}\right)
where \mathbf{z}=\boldsymbol{\Sigma }_{\mathbf{X}}^{-1/2}(\mathbf{X}-\boldsymbol{\mu}_{\mathbf{X}}),
\mathbf{B}_{k} denote the T-Bell Polynomials and \varphi denotes the
multivariate normal density. The case n=1 provides and approximation
to the density of \mathbf{X} and can be compared to the GramCharlier approximation.
Edgeworth(X, cum, n = 1)
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. |
n |
the number of terms in the mean |
The vector of the Edgeworth density evaluated at X
Gy.Terdik, Multivariate statistical methods - Going beyond the linear, Springer 2021. Section 4.7.
Other Approximations:
GramCharlier(),
IntEdgeworth(),
IntGramCharlier(),
MTCE()
# Edgeworth 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<-Edgeworth(X[1:50,],cum=EC,n=1)
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