Ja. Lechner et al., ASSESSMENT OF PEAKS OVER THRESHOLD METHODS FOR ESTIMATING EXTREME-VALUE DISTRIBUTION TAILS, Structural safety, 12(4), 1993, pp. 305-314
In the past twenty years a vast new body of extreme value theory was d
eveloped, referred to as 'peaks over threshold modeling.' This theory
allows the use in the analysis of all data exceeding a sufficiently hi
gh threshold, a feature that may result in improved extreme value esti
mates. The application of the theory depends upon the performance of m
ethods for estimating the distribution parameters Corresponding to any
given set of extreme data. We present a comparative assessment of the
performance of three such methods. The assessment is based on Monte C
arlo simulations from populations with four distributions: Gumbel, Wei
bull, generalized Pareto, and normal. The simulation results showed th
at the de Haan and the Conditional Mean Exceedance (CME) methods perfo
rmed consistently better than the Pickands method (NIST implementation
). For the distributions, parameter values, and mean recurrence interv
als assumed in this work, the CME method outperformed the de Haan meth
od only when the percent estimation errors were about one percent or s
maller, a case unlikely to be encountered in wind engineering practice
.