rmpareto {graphicalExtremes} | R Documentation |
Sampling of a multivariate Pareto distribution
Description
Simulates exact samples of a multivariate Pareto distribution.
Usage
rmpareto(
n,
model = c("HR", "logistic", "neglogistic", "dirichlet"),
d = NULL,
par
)
Arguments
n |
Number of simulations. |
model |
The parametric model type; one of:
|
d |
Dimension of the multivariate Pareto distribution.
In some cases this can be |
par |
Respective parameter for the given
|
Details
The simulation follows the algorithm in Engelke and Hitz (2020). For details on the parameters of the Huesler-Reiss, logistic and negative logistic distributions see Dombry et al. (2016), and for the Dirichlet distribution see Coles and Tawn (1991).
Value
Numeric matrix of simulations of the
multivariate Pareto distribution.
References
Coles S, Tawn JA (1991).
“Modelling extreme multivariate events.”
J. R. Stat. Soc. Ser. B Stat. Methodol., 53, 377–392.
Dombry C, Engelke S, Oesting M (2016).
“Exact simulation of max-stable processes.”
Biometrika, 103, 303–317.
Engelke S, Hitz AS (2020).
“Graphical models for extremes (with discussion).”
J. R. Stat. Soc. Ser. B Stat. Methodol., 82, 871–932.
See Also
Other sampling functions:
rmpareto_tree()
,
rmstable_tree()
,
rmstable()
Examples
## A 4-dimensional HR distribution
n <- 10
d <- 4
G <- cbind(
c(0, 1.5, 1.5, 2),
c(1.5, 0, 2, 1.5),
c(1.5, 2, 0, 1.5),
c(2, 1.5, 1.5, 0)
)
rmpareto(n, "HR", d = d, par = G)
## A 3-dimensional logistic distribution
n <- 10
d <- 3
theta <- .6
rmpareto(n, "logistic", d, par = theta)
## A 5-dimensional negative logistic distribution
n <- 10
d <- 5
theta <- 1.5
rmpareto(n, "neglogistic", d, par = theta)
## A 4-dimensional Dirichlet distribution
n <- 10
d <- 4
alpha <- c(.8, 1, .5, 2)
rmpareto(n, "dirichlet", d, par = alpha)