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Estimating return levels from serially dependent extremes

Lookup NU author(s): Dr Lee Fawcett, Dr David WalshawORCiD


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In this paper, we investigate the relationship between return levels of a process and the strength of serial correlation present in the extremes of that process. Estimates of long period return levels are often used as design requirements, and peaks over thresholds analyses have, in the past, been used to obtain such estimates. However, analyses based on such declustering schemes are extremely wasteful of data, often resulting in great estimation uncertainty represented by very wide confidence intervals. Using simulated data, we show that—provided the extremal index is estimated appropriately—using all threshold excesses can give more accurate and precise estimates of return levels, allowing us to avoid altogether the sometimes arbitrary process of cluster identification. We then apply our method to two data examples concerning sea-surge and wind-speed extremes.

Publication metadata

Author(s): Fawcett L, Walshaw D

Publication type: Article

Publication status: Published

Journal: Environmetrics

Year: 2012

Volume: 23

Issue: 3

Pages: 272-283

Print publication date: 27/03/2012

ISSN (print): 1180-4009

ISSN (electronic): 1099-095X

Publisher: John Wiley & Sons Ltd.


DOI: 10.1002/env.2133


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