stdf: Non-parametric estimators of the STDF

In ReIns: Functions from "Reinsurance: Actuarial and Statistical Aspects"

Non-parametric estimators of the STDF

Description

Non-parametric estimators of the stable tail dependence function (STDF): \hat{l}_k(x) and \tilde{l}_k(x).

Usage

stdf(x, k, X, alpha = 0.5)

stdf2(x, k, X)

Arguments

x	A d-dimensional point to estimate the STDF in.
k	Value of the tail index k.
X	A data matrix of dimensions n by d with observations in the rows.
alpha	The parameter \alpha of the estimator \hat{l}_k(x) (stdf), default is 0.5. This argument is not used in stdf2.

Details

The stable tail dependence function in x can be estimated by

\hat{l}_k(x) = 1/k \sum_{i=1}^n 1_{\{\exists j\in\{1,\ldots, d\}: \hat{F}_j(X_{i,j})>1-k/n x_j\}}

with

\hat{F}_j(X_{i,j})=(R_{i,j}-\alpha)/n

where R_{i,j} is the rank of X_{i,j} among the n observations in the j-th dimension:

R_{i,j}=\sum_{m=1}^n 1_{\{X_{m,j}\le X_{i,j}\}}.

This estimator is implemented in stdf.

The second estimator is given by

\tilde{l}_k(x) = 1/k \sum_{i=1}^n 1_{\{X_{i,1}\ge X^{(1)}_{n-[kx_1]+1,n} or \ldots or X_{i,d}\ge X^{(d)}_{n-[kx_d]+1,n}\}}

where X_{i,n}^{(j)} is the i-th smallest observation in the j-th dimension. This estimator is implemented in stdf2.

See Section 4.5 of Beirlant et al. (2016) for more details.

Value

stdf returns the estimate \hat{l}_k(x) and stdf2 returns the estimate \tilde{l}_k(x).

Author(s)

Tom Reynkens

References

Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.

Beirlant J., Goegebeur Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications, Wiley Series in Probability, Wiley, Chichester.

Examples

# Generate data matrix
X <- cbind(rpareto(100,2), rpareto(100,3))

# Tail index
k <- 20

# Point to evaluate the STDF in
x <- c(2,3)

# First estimate
stdf(x, k, X)

# Second estimate
stdf2(x, k, X)

ReIns documentation built on April 3, 2025, 6:04 p.m.