Basin-wide spatial conditional extremes for severe ocean storms
From MaRDI portal
Publication:2028585
DOI10.1007/s10687-020-00389-wzbMath1473.60087OpenAlexW3047173263WikidataQ114210186 ScholiaQ114210186MaRDI QIDQ2028585
Emma Ross, Jonathan A. Tawn, Rob Shooter, Philip Jonathan
Publication date: 1 June 2021
Published in: Extremes (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10687-020-00389-w
Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)
Related Items
Environmental contours as Voronoi cells, Joint stochastic simulation of extreme coastal and offshore significant wave heights, Extremal characteristics of conditional models, Modelling extremes of spatial aggregates of precipitation using conditional methods
Cites Work
- Unnamed Item
- Unnamed Item
- A hierarchical max-stable spatial model for extreme precipitation
- The generalized Pareto process; with a view towards application and simulation
- Models for stationary max-stable random fields
- Estimation of the conditional distribution of a multivariate variable given that one of its components is large: additional constraints for the Heffernan and Tawn model
- Limit laws for random vectors with an extreme component
- Dependence modelling for spatial extremes
- Statistics for near independence in multivariate extreme values
- Extreme values of independent stochastic processes
- High-dimensional peaks-over-threshold inference
- Atmospheric and Oceanic Fluid Dynamics
- Inference for Clusters of Extreme Values
- Bayesian Measures of Model Complexity and Fit
- A Conditional Approach for Multivariate Extreme Values (with Discussion)
- Modeling Spatial Processes with Unknown Extremal Dependence Class
- Modelling Across Extremal Dependence Classes
- Dependence measures for extreme value analyses
- Statistical modeling of spatial extremes
