beed.boot {ExtremalDep}  R Documentation 
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.
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)
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; 
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 maxlikelihood fitting of the GEV distributions is implemented when margin="est"
. Otherwise it refers to marginal parametric GEV theorethical distributions (margin = "exp", "frechet", "gumbel"
).
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. 
Simone Padoan, simone.padoan@unibocconi.it, https://mypage.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com https://www.borisberanger.com/; Giulia Marcon, giuliamarcongm@gmail.com
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, 117.
if (interactive()){
x < ExtremalDep:::simplex(2)
data < rbvevd(50, dep = 0.4, model = "log", mar1 = c(1,1,1))
boot < beed.boot(data, x, 2, "md", "emp", 20, 500)
}