Robust quantile estimation under bivariate extreme value models

Publikation: Beitrag in FachzeitschriftArtikelForschung

Autoren

  • Sojung Kim
  • Heelang Rye
  • Kyoung-Kuk Kim

Externe Organisationen

  • Korea Institute of Science and Technology
  • Korea Advanced Institute of Science and Technology (KAIST)
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Details

OriginalspracheEnglisch
Seiten (von - bis)55 - 83
Seitenumfang29
FachzeitschriftExtremes
Jahrgang23
Ausgabenummer1
Frühes Online-Datum5 Sept. 2019
PublikationsstatusVeröffentlicht - März 2020

Abstract

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.

ASJC Scopus Sachgebiete

Zitieren

Robust quantile estimation under bivariate extreme value models. / Kim, Sojung; Rye, Heelang; Kim, Kyoung-Kuk.
in: Extremes, Jahrgang 23, Nr. 1, 03.2020, S. 55 - 83.

Publikation: Beitrag in FachzeitschriftArtikelForschung

Kim S, Rye H, Kim KK. Robust quantile estimation under bivariate extreme value models. Extremes. 2020 Mär;23(1):55 - 83. Epub 2019 Sep 5. doi: 10.1007/s10687-019-00362-2
Kim, Sojung ; Rye, Heelang ; Kim, Kyoung-Kuk. / Robust quantile estimation under bivariate extreme value models. in: Extremes. 2020 ; Jahrgang 23, Nr. 1. S. 55 - 83.
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