danielsson: A Double Bootstrap Procedure for Choosing the Optimal Sample...

In tea: Threshold Estimation Approaches

Description

An Implementation of the procedure proposed in Danielsson et al. (2001) for selecting the optimal sample fraction in tail index estimation.

Usage

danielsson(data, B = 500, epsilon = 0.9)

Arguments

data	vector of sample data
B	number of Bootstrap replications
epsilon	gives the amount of the first resampling size n1 by choosing n1 = n^epsilon. Default is set to epsilon=0.9

Details

The Double Bootstrap procedure simulates the AMSE criterion of the Hill estimator using an auxiliary statistic. Minimizing this statistic gives a consistent estimator of the sample fraction k/n with k the optimal number of upper order statistics. This number, denoted k0 here, is equivalent to the number of extreme values or, if you wish, the number of exceedances in the context of a POT-model like the generalized Pareto distribution. k0 can then be associated with the unknown threshold u of the GPD by choosing u as the n-k0th upper order statistic. For more information see references.

Value

second.order.par	gives an estimation of the second order parameter rho.
k0	optimal number of upper order statistics, i.e. number of exceedances or data in the tail
threshold	the corresponding threshold
tail.index	the corresponding tail index

References

Danielsson, J. and Haan, L. and Peng, L. and Vries, C.G. (2001). Using a bootstrap method to choose the sample fraction in tail index estimation. Journal of Multivariate analysis, 2, 226-248.

Examples

data=rexp(100)
danielsson(data, B=200)

Example output

$sec.order.par
[1] -0.6255156

$k0
[1] 8

$threshold
[1] 2.457104

$tail.index
[1] 2.466126

tea documentation built on April 19, 2020, 3:57 p.m.