Rights statement: 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.
Accepted author manuscript, 596 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Rights statement: 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.
Accepted author manuscript, 249 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Modelling across extremal dependence classes
AU - Wadsworth, Jenny
AU - Tawn, Jonathan Angus
AU - Davison, Anthony
AU - Elton, Daniel Mark
N1 - 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.
PY - 2017/1
Y1 - 2017/1
N2 - 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.
AB - 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.
KW - asymptotic independence
KW - censored likelihood
KW - conditional extremes
KW - dependence modelling
KW - extreme value theory
KW - multivariate regular variation
U2 - 10.1111/rssb.12157
DO - 10.1111/rssb.12157
M3 - Journal article
VL - 79
SP - 149
EP - 175
JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)
JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)
SN - 1369-7412
IS - 1
ER -