The methodology proposed by Beirlant, Vynckier and Teugels (1996)
uses an asymptotic expansion of the mean squared error for Hill's estimator given
a random sample with Pareto tails and positive shape, using an exponential regression.
The value of k
is selected to minimize the mean squared error given optimal weighting scheme. This
depends on the order of regular variation \(\rho\), which is obtained based on the slope of the difference
in Hill estimators, suitably reweighted. The iterative procedure of Beirlant et al. alternates between parameter estimation
until convergence. It returns the Hill shape estimate, the number of higher order statistic, the parameter rho
and
estimates of the standard error of the shape and the mean squared error, based on the ultimate parameter values.
Since the weights can become negative, there is no guarantee that the mean squared error estimate is positive, nor that
the estimated value of \(\rho\) is nonpositive.
Arguments
- xdat
[vector] sample of exceedances
- maxiter
[int] maximum number of iteration
- tol
[double] tolerance for difference in value of \(k\) for the fixed point
- kmin
[int] minimum number of exceedances for the estimator
- kmax
[int] maximum number of exceedances for the estimator
- ...
additional arguments, currently ignored
Value
a list with components
shape
the Hill estimator of the shape, based on thek
largest order statisticsk0
number of high order statistics for estimation of the shape using Hill's estimatorrho
estimate of the second order regular variation parametermse
mean squared error estimate of the shape parameterse
standard error of the shape parameterconvergence
logical; ifTRUE
, indicates that the method converged to a fixed point withintol
before reaching the maximum number of iterationsmaxiter
References
Beirlant, J., Vynckier, P., & Teugels, J. L. (1996). Excess Functions and Estimation of the Extreme-Value Index. Bernoulli, 2(4), 293–318. doi:10.2307/3318416
Examples
# Simulate Pareto data - log(xdat) is exponential with rate 2
xdat <- rgp(n = 200, loc = 1, scale = 0.5, shape = 0.5)
(thselect.expgqt(xdat))
#> Threshold selection method: Beirlant, Vynckier and Teugels (1996)
#> Generalized quantile threshold selection
#>
#> Selected threshold: 3.829
#> Number of exceedances: 22
#> Shape estimate: 0.001