Modelling across extremal dependence classes

Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available statistical models suit one or other of these cases, but not both, resulting in a stage in the inference that is unaccounted for but can substantially impact subsequent extrapolation. Existing modelling solutions to this problem are either applicable only on subdomains or appeal to multiple limit theories. We introduce a unified representation for bivariate extremes that encompasses a wide variety of dependence scenarios and applies when at least one variable is large. Our representation motivates a parametric model that encompasses both dependence classes. We implement a simple version of this model and show that it performs well in a range of settings.


Publié dans:
Journal Of The Royal Statistical Society Series B-Statistical Methodology, 79, 1, 149-175
Année
2017
Publisher:
Hoboken, Wiley-Blackwell
ISSN:
1369-7412
Mots-clefs:
Laboratoires:




 Notice créée le 2017-03-27, modifiée le 2018-09-13


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