dot-fit.gpd.rob: Robust threshold selection of Dupuis

In mev: Modelling of Extreme Values

Robust threshold selection of Dupuis

Description

The optimal bias-robust estimator (OBRE) for the generalized Pareto. This function returns robust estimates and the associated weights.

Usage

.fit.gpd.rob(dat, thresh, k = 4, tol = 1e-05, show = FALSE)

Arguments

dat	a numeric vector of data
thresh	threshold parameter
k	bound on the influence function; the constant k is a robustness parameter (higher bounds are more efficient, low bounds are more robust). Default to 4.
tol	numerical tolerance for OBRE weights iterations.
show	logical: should diagnostics and estimates be printed. Default to FALSE.

Value

a list with the same components as fit.gpd, in addition to

• estimate:optimal bias-robust estimates of the scale and shape parameters.

• weights:vector of OBRE weights.

References

Dupuis, D.J. (1998). Exceedances over High Thresholds: A Guide to Threshold Selection, Extremes, 1(3), 251–261.

See Also

fit.gpd

Examples

dat <- rexp(100)
.fit.gpd.rob(dat, 0.1)

mev documentation built on April 20, 2023, 5:10 p.m.