Modeling nonstationary extremes of storm severity:Comparing parametric and semiparametric inference

Konzen, E. and Neves, C. and Jonathan, P. (2021) Modeling nonstationary extremes of storm severity:Comparing parametric and semiparametric inference. Environmetrics, 32 (4). ISSN 1180-4009

Full text not available from this repository.


This article compares the modeling of nonstationary extreme events using parametric models with local parametric and semiparametric approaches also motivated by extreme value theory. Specifically, three estimators are compared based on (a) (local) semiparametric moment estimation, (b) (local) maximum likelihood estimation, and (c) spline-based maximum likelihood estimation. Inference is performed in a sequential manner, highlighting the synergies between the different approaches to estimating extreme quantiles, including the T-year level and right endpoint when finite. We present a novel heuristic to estimate nonstationary extreme value threshold with exceedances varying on a circular domain, and hypothesis-testing procedures for identifying max-domain of attraction in the nonstationary setting. Bootstrapping is used to estimate nonstationary confidence bounds throughout. We provide step-by-step guides for estimation, and explore the different inference strategies in application to directional modeling of hindcast storm peak significant wave heights recorded in the North Sea. © 2021 The Authors. Environmetrics published by John Wiley & Sons, Ltd.

Item Type:
Journal Article
Journal or Publication Title:
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
27 Jul 2021 09:45
Last Modified:
22 Nov 2022 09:54