RT: Adaptive choice of the optimal sample fraction in tail index...
In tea: Threshold Estimation Approaches

Description Usage Arguments Details Value References Examples

An implementation of the minimization criterion proposed in Reiss & Thomas (2007).

1	RT(data, beta = 0, kmin = 2)

`data`	vector of sample data
`beta`	a factor for weighting the expression below. Default is set to `beta=0`
`kmin`	gives a minimum value for `k`. Default ist set to `kmin=2`

The procedure proposed in Reiss & Thomas (2007) chooses the lowest upper order statistic k to minimize the expression 1/k sum_i=1^k i^beta |gamma_i-median(gamma_1,...,gamma_k)| or an alternative of that by replacing the absolute deviation with a squared deviation and the median just with gamma_k, where gamma denotes the Hill estimator

`k0`	optimal number of upper order statistics, i.e. number of exceedances or data in the tail for both metrics, i.e. the absolute and squared deviation.
`threshold`	the corresponding thresholds.
`tail.index`	the corresponding tail indices

Reiss, R.-D. and Thomas, M. (2007). Statistical Analysis of Extreme Values: With Applications to Insurance, Finance, Hydrology and Other Fields. Birkhauser, Boston.