rparpcshr {mev} | R Documentation |
Simulation of generalized Huesler-Reiss Pareto vectors via composition sampling
Description
Sample from the generalized Pareto process associated to Huesler-Reiss spectral profiles.
For the Huesler-Reiss Pareto vectors, the matrix Sigma
is utilized to build Q
viz.
Q = \Sigma^{-1} - \frac{\Sigma^{-1}\mathbf{1}_d\mathbf{1}_d^\top\Sigma^{-1}}{\mathbf{1}_d^\top\Sigma^{-1}\mathbf{1}_d}.
The location vector m
and Sigma
are the parameters of the underlying log-Gaussian process.
Usage
rparpcshr(n, u, alpha, Sigma, m)
Arguments
n |
sample size |
u |
vector of marginal location parameters (must be strictly positive) |
alpha |
vector of shape parameters (must be strictly positive). |
Sigma |
covariance matrix of process, used to define |
m |
location vector of Gaussian distribution. |
Value
n
by d matrix of observations
References
Ho, Z. W. O and C. Dombry (2019), Simple models for multivariate regular variations and the Huesler-Reiss Pareto distribution, Journal of Multivariate Analysis (173), p. 525-550, doi:10.1016/j.jmva.2019.04.008
Examples
D <- 20L
coord <- cbind(runif(D), runif(D))
di <- as.matrix(dist(rbind(c(0, ncol(coord)), coord)))
semivario <- function(d, alpha = 1.5, lambda = 1){(d/lambda)^alpha}
Vmat <- semivario(di)
Sigma <- outer(Vmat[-1, 1], Vmat[1, -1], '+') - Vmat[-1, -1]
m <- Vmat[-1,1]
## Not run:
samp <- rparpcshr(n = 100, u = c(rep(1, 10), rep(2, 10)),
alpha = seq(0.1, 1, length = 20), Sigma = Sigma, m = m)
## End(Not run)