Threshold selection for wave heights: asymptotic methods based on L-moments
Jessica Silva Lomba, Maria Isabel Fraga Alves
TL;DR
This paper introduces two automatic threshold selection methods for extreme wave height analysis using asymptotic properties of L-moments and compares their performance to existing heuristics, demonstrating improved effectiveness on real data.
Contribution
The paper develops novel asymptotic methods for threshold selection in extreme value analysis based on L-moments, enhancing the accuracy of peak-over-threshold modeling.
Findings
Methods outperform existing heuristics on wave height data
L-moment-based approaches provide reliable threshold estimates
Performance evaluation on real datasets confirms effectiveness
Abstract
Two automatic threshold selection (TS) methods for Extreme Value analysis under a peaks-over-threshold (POT) approach are presented and evaluated, both built on: fitting the Generalized Pareto distribution (GPd) to excesses' samples over candidate levels ; the GPd-specific relation between L-skewness and L-kurtosis; the asymptotic behaviour of the matching L-statistics. Performance is illustrated on significant wave heights data sets and compared to the L-moment-based heuristic in [10], which is found to be favorable. PUBLISHED VERSION AVAILABLE AT: https://www.spestatistica.pt/storage/app/uploads/public/609/28f/6d0/60928f6d08a0c016386627.pdf
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Statistical Methods and Inference · Hydrology and Drought Analysis