Pseudo random number generation from a mulitple-scaled generalized hyperbolic distribution (MSGHD).
Generate n pseudo random numbers from a p dimensional mulitple-scaled generalized hyperbolic distribution.
rMSGHD(n,p, mu=rep(0,p),alpha=rep(0,p),sigma=diag(p),omegav=rep(1,p),lambdav=rep(0.5,p))
n |
number of observations. |
p |
number of variables. |
mu |
(optional) the p dimensional mean |
alpha |
(optional) the p dimensional skewness parameter alpha |
sigma |
(optional) the p x p dimensional scale matrix |
omegav |
(optional) the p dimensional concentration parameter omega |
lambdav |
(optional) the p dimensional index parameter lambda |
The default values are: 0 for the mean and the skweness parameter alpha, diag(p) for sigma, 1 for omega, and 0.5 for lambda.
A n times p matrix of numbers psudo randomly generated from a generilzed hyperbolic distribution
Cristina Tortora, Aisha ElSherbiny, Ryan P. Browne, Brian C. Franczak, and Paul D. McNicholas. Maintainer: Cristina Tortora <cristina.tortora@sjsu.edu>
C. Tortora, B.C. Franczak, R.P. Browne, and P.D. McNicholas (2019). A Mixture of Coalesced Generalized Hyperbolic Distributions. Journal of Classification (to appear).
data=rMSGHD(300,2,alpha=c(2,-2),omegav=c(2,2)) plot(data)
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