A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference
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.
Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference
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.
Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference
Konzen, Evandro (author) / Neves, Cláudia (author) / Jonathan, Philip (author)
Environmetrics ; 32
2021-06-01
23 pages
Article (Journal)
Electronic Resource
English
Bayesian inference for nonstationary marginal extremes
| Wiley | 2016
On spatial conditional extremes for ocean storm severity
| Wiley | 2019
Developing a Storm Severity Index
| British Library Online Contents | 2005
Determinants of flood adaptation: Parametric and semiparametric assessment
| DOAJ | 2021
Parametric, semiparametric and nonparametric models of urban growth
| Elsevier | 2022