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 n \times d matrix of component-wise maxima. x m \times d design matrix where the dependence function is evaluated, see Details. d postive integer (greater than or equal to two) indicating the number of variables (d=3 by default). 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 (k=13 by default). nboot postive integer indicating the number of bootstrap replicates (nboot=500 by default). y numeric vector (of size m) with an initial estimate of the Pickands function. If NULL, The initial estimation is performed by using the estimation method chosen in est. print logical; FALSE by default. If TRUE the number of the iteration is printed.

### 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 nboot columns that reports the estimates of the Pickands function for each data resampling. beta matrix of estimated polynomial coefficients. Each column corresponds to a data resampling.

### 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.

beed, beed.confband.

### Examples

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)
}


[Package ExtremalDep version 0.0.3-5 Index]