Kernel-based threshold selection of Goegebeur, Beirlant and de Wet (2008)
Source:R/kernel_exponential_thselect.R
      thselect.gbw.RdKernel-based threshold selection of Goegebeur, Beirlant and de Wet (2008)
Value
a list with elements
- k0: number of exceedances
- shape: Hill's shape estimate
- rho: second-order regular variation parameter estimate
- gof: goodness-of-fit statistic for the chosen threshold.
References
Goegebeur , Y., Beirlant , J., and de Wet , T. (2008). Linking Pareto-Tail Kernel Goodness-of-fit Statistics with Tail Index at Optimal Threshold and Second Order Estimation. REVSTAT-Statistical Journal, 6(1), 51–69. <doi:10.57805/revstat.v6i1.57>
Examples
xdat <- rgp(n = 1000, scale = 2, shape = 0.5)
(thselect.gbw(xdat, kmax = 500))
#> Threshold selection method: Jackson kernel 
#> Goegebeur, Beirlant and de Wet (2008)
#> Second-order regular variation index (gbw estimator):  -1.192 
#> Number of exceedances: 140 
#> Selected threshold: 6.42 
#> Shape estimate: 0.724