Strutures, Safety and Reliability

Extreme Value Statistics of Wind Speed Data by the POT and ACER Methods

[+] Author and Article Information
A. Naess

Department of Mathematical Sciences, Centre for Ships and Ocean Structures, Norwegian University of Science and Technology, NO-7491 Trondheim, Norwayarvidn@math.ntnu.no

E. Haug

Department of Mathematical Sciences, Programme on Industrial Mathematics, Norwegian University of Science and Technology, NO-7491 Trondheim, Norwayevenha@stud.ntnu.no

J. Offshore Mech. Arct. Eng 132(4), 041604 (Sep 23, 2010) (7 pages) doi:10.1115/1.4001419 History: Received December 29, 2008; Revised February 03, 2010; Published September 23, 2010; Online September 23, 2010

The paper describes a novel method for predicting the appropriate extreme value distribution derived from an observed time series. The method is based on introducing a cascade of conditioning approximations to the exact extreme value distribution. This allows for a rational way of capturing dependence effects in the time series. The performance of the method is compared with that of the peaks-over-threshold method.

Copyright © 2010 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 2

Empirical MEF for data with 3 days declustering

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Figure 4

Plot of log ϵ̂k(η) against η is the number of standard deviations for k=1,…,6, k=1 uppermost, then k=2, etc.

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Figure 6

Plot of log ϵ̂k(η) against η for k=1 for the optimized parameter values with 95% confidence band.

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Figure 5

Plot of log|log ϵ̂k(η)| against log(η−b) for k=1 for the optimized parameter values

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Figure 3

Plots of the ML estimates of the parameters σ̃∗ and ξ

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Figure 1

Plot of the wind speed data given in m/s



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