mean-vcov-skew-kurt-methods {ghyp} | R Documentation |
Expected value, variance-covariance, skewness and kurtosis of generalized hyperbolic distributions
Description
The function mean
returns the expected value. The function
vcov
returns the variance in the univariate case and the
variance-covariance matrix in the multivariate case. The functions
ghyp.skewness
and ghyp.kurtosis
only work for univariate
generalized hyperbolic distributions.
Usage
## S4 method for signature 'ghyp'
mean(x)
## S4 method for signature 'ghyp'
vcov(object)
ghyp.skewness(object)
ghyp.kurtosis(object)
Arguments
x , object |
An object inheriting from class
|
Details
The functions ghyp.skewness
and ghyp.kurtosis
are based
on the function ghyp.moment
. Numerical integration will
be used in case a Student.t or variance gamma distribution is
submitted.
Value
Either the expected value, variance, skewness or kurtosis.
Author(s)
David Luethi
See Also
ghyp
, ghyp-class
, Egig
to
compute the expected value and the variance of the generalized inverse gaussian
mixing distribution distributed and its special cases.
Examples
## Univariate: Parametric
vg.dist <- VG(lambda = 1.1, mu = 10, sigma = 10, gamma = 2)
mean(vg.dist)
vcov(vg.dist)
ghyp.skewness(vg.dist)
ghyp.kurtosis(vg.dist)
## Univariate: Empirical
vg.sim <- rghyp(10000, vg.dist)
mean(vg.sim)
var(vg.sim)
## Multivariate: Parametric
vg.dist <- VG(lambda = 0.1, mu = c(55, 33), sigma = diag(c(22, 888)), gamma = 1:2)
mean(vg.dist)
vcov(vg.dist)
## Multivariate: Empirical
vg.sim <- rghyp(50000, vg.dist)
colMeans(vg.sim)
var(vg.sim)
[Package ghyp version 1.6.4 Index]