mvrggd | R Documentation |
Produces one or more samples from a multivariate (p
variables) generalized Gaussian distribution (MGGD).
mvrggd(n = 1 , mu, Sigma, beta, tol = 1e-6)
n |
integer. Number of observations. |
mu |
length |
Sigma |
symmetric, positive-definite square matrix of order |
beta |
positive real number. The shape of the distribution. |
tol |
tolerance (relative to largest variance) for numerical lack of positive-definiteness in Sigma. |
A sample from a centered MGGD with dispersion matrix \Sigma
and shape parameter \beta
can be generated using:
\displaystyle{X = \tau \ \Sigma^{1/2} \ U}
where U
is a random vector uniformly distributed on the unit sphere and
\tau
is such that \tau^{2\beta}
is generated from a distribution Gamma
with shape parameter \displaystyle{\frac{p}{2\beta}}
and scale parameter 2
.
This property is used to generate a sample from a MGGD.
A matrix with p
columns and n
rows.
Pierre Santagostini, Nizar Bouhlel
E. Gomez, M. Gomez-Villegas, H. Marin. A Multivariate Generalization of the Power Exponential Family of Distribution. Commun. Statist. 1998, Theory Methods, col. 27, no. 23, p 589-600. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610929808832115")}
mvdggd
: probability density of a MGGD..
estparmvggd
: estimation of the parameters of a MGGD.
mu <- c(0, 0, 0)
Sigma <- matrix(c(0.8, 0.3, 0.2, 0.3, 0.2, 0.1, 0.2, 0.1, 0.2), nrow = 3)
beta <- 0.74
mvrggd(100, mu, Sigma, beta)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.