beed.boot {ExtremalDep} | R Documentation |
Bootstrap Resampling and Bernstein Estimation of Extremal Dependence
Description
Computes nboot
estimates of the Pickands dependence function for multivariate data (using the Bernstein polynomials approximation method) on the basis of the bootstrap resampling of the data.
Usage
beed.boot(data, x, d = 3, est = c("ht","md","cfg","pick"),
margin=c("emp", "est", "exp", "frechet", "gumbel"), k = 13,
nboot = 500, y = NULL, print = FALSE)
Arguments
data |
|
x |
|
d |
postive integer (greater than or equal to two) indicating the number of variables ( |
est |
string denoting the preliminary estimation method (see Details). |
margin |
string denoting the type marginal distributions (see Details). |
k |
postive integer denoting the order of the Bernstein polynomial ( |
nboot |
postive integer indicating the number of bootstrap replicates ( |
y |
numeric vector (of size |
print |
logical; |
Details
Standard bootstrap is performed, in particular estimates of the Pickands dependence function are provided for each data resampling.
Most of the settings are the same as in the function beed
.
An empirical transformation of the marginals is performed when margin="emp"
. A max-likelihood fitting of the GEV distributions is implemented when margin="est"
. Otherwise it refers to marginal parametric GEV theorethical distributions (margin = "exp", "frechet", "gumbel"
).
Value
A |
numeric vector of the estimated Pickands dependence function. |
bootA |
matrix with |
beta |
matrix of estimated polynomial coefficients. Each column corresponds to a data resampling. |
Author(s)
Simone Padoan, simone.padoan@unibocconi.it, https://faculty.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com https://www.borisberanger.com; Giulia Marcon, giuliamarcongm@gmail.com
References
Marcon, G., Padoan, S.A., Naveau, P., Muliere, P. and Segers, J. (2017) Multivariate Nonparametric Estimation of the Pickands Dependence Function using Bernstein Polynomials. Journal of Statistical Planning and Inference, 183, 1-17.
See Also
Examples
x <- simplex(2)
data <- evd::rbvevd(50, dep = 0.4, model = "log", mar1 = c(1,1,1))
boot <- beed.boot(data, x, 2, "md", "emp", 20, 500)