distr_CFUSN_Rand {MultiStatM} | R Documentation |
Generate random d-vectors from the multivariate Canonical Fundamental Skew-Normal (CFUSN) distribution
distr_CFUSN_Rand(n, Delta)
n |
The number of variates to be generated |
Delta |
Correlation matrix, the skewness matrix Delta |
A random matrix n \times d
Gy.Terdik, Multivariate statistical methods - Going beyond the linear, Springer 2021 (5.5) p.247
S. R. Jammalamadaka, E. Taufer, Gy. Terdik. On multivariate skewness and kurtosis. Sankhya A, 83(2), 607-644.
Other Random generation:
distr_CFUSSD_Rand()
,
distr_SkewNorm_Rand()
,
distr_Uni_Rand()
Other Multivariate distributions:
distr_CFUSN_MomCum_Th()
,
distr_CFUSSD_Rand()
,
distr_SkewNorm_EVSK_Th()
,
distr_SkewNorm_MomCum_Th()
,
distr_SkewNorm_Rand()
,
distr_UniAbs_EVSK_Th()
,
distr_Uni_EVSK_Th()
,
distr_Uni_MomCum_Th()
,
distr_Uni_Rand()
,
distr_ZabsM_MomCum_Th()
,
distr_Zabs_MomCum_Th()
d <- 2; p <- 3
Lamd <- matrix(sample(1:50-25, d*p), nrow=d)
ieg<- eigen(diag(p)+t(Lamd)%*%Lamd)
V <- ieg$vectors
Delta <-Lamd %*% V %*% diag(1/sqrt(ieg$values)) %*% t(V)
x<-distr_CFUSN_Rand(20,Delta)