Modelling across extremal dependence classes

Wadsworth, Jenny and Tawn, Jonathan Angus and Davison, Anthony and Elton, Daniel Mark (2017) Modelling across extremal dependence classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79 (1). pp. 149-175. ISSN 1369-7412

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Abstract

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 sub-domains, 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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Additional Information:
This is the peer reviewed version of the following article: Wadsworth, J. L., Tawn, J. A., Davison, A. C. and Elton, D. M. (2017), Modelling across extremal dependence classes. J. R. Stat. Soc. B, 79: 149–175. doi:10.1111/rssb.12157 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/rssb.12157/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
76796
Deposited By:
Deposited On:
24 Nov 2015 10:04
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 03:43