The Multivariate Gaussian Tail Model: An Application to Oceanographic Data

DOI10.1111/1467-9876.00177zbMath0944.62110OpenAlexW2055463720MaRDI QIDQ4949584

Jonathan A. Tawn, Paola Bortot, Stuart G. Coles

Publication date: 24 September 2000

Published in: Journal of the Royal Statistical Society Series C: Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9876.00177

zbMATH Keywords

oceanographyGaussian distributionasymptotic independenceextreme value theorythreshold modelsmultivariate extreme value distributionstructure variable methodjoint probabilities method

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Hydrology, hydrography, oceanography (86A05)

Related Items (11)

Modeling spatial extremes using normal mean-variance mixtures ⋮ A note on the convergence of trivariate extreme order statistics and extension ⋮ Extreme dependence models based on event magnitude ⋮ Hierarchical Space-Time Modeling of Asymptotically Independent Exceedances With an Application to Precipitation Data ⋮ A New Class of Models for Bivariate Joint Tails ⋮ Geostatistics of dependent and asymptotically independent extremes ⋮ A Conditional Approach for Multivariate Extreme Values (with Discussion) ⋮ Examples for the coefficient of tail dependence and the domain of attraction of a bivariate extreme value distribution ⋮ Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures ⋮ Semiparametric bivariate modelling with flexible extremal dependence ⋮ Likelihood estimators for multivariate extremes

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