Robust quantile estimation under bivariate extreme value models

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In risk quantification of extreme events in multiple dimensions, a correct specification of the dependence structure among variables is difficult due to the limited size of effective data. This paper studies the problem of estimating quantiles for bivariate extreme value distributions, considering that an estimated Pickands dependence function may deviate from the truth within some fixed distance. Our method thus finds optimal upper and lower bounds for the true but unknown dependence function, based on which robust quantile bounds are obtained. A simulation study shows the usefulness of our robust estimates that can supplement traditional error estimation methods.
Publisher
SPRINGER
Issue Date
2020-03
Language
English
Article Type
Article
Citation

EXTREMES, v.23, no.1, pp.55 - 83

ISSN
1386-1999
DOI
10.1007/s10687-019-00362-2
URI
http://hdl.handle.net/10203/272368
Appears in Collection
IE-Journal Papers(저널논문)
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