Publication detail

Sensitivity assessment of extremal index maxima estimates in the estimation of extreme values for stationary time series

HOLEŠOVSKÝ, J.

Original Title

Sensitivity assessment of extremal index maxima estimates in the estimation of extreme values for stationary time series

English Title

Sensitivity assessment of extremal index maxima estimates in the estimation of extreme values for stationary time series

Type

conference paper

Language

en

Original Abstract

Extremal index is the primary measure of local dependence of extreme values, and plays thus an important role in extreme value estimation for stationary processes. The maxima estimators are often preferred in practical situations. These estimators are based on properties of the block maxima asymptotically characterized by the Generalized extreme value distribution. In contrast to the other methods, the maxima estimators gain advantage in stability to the choice of auxiliary parameters. Still the main part of maxima methods is selection of a proper approximation to marginal distribution of the underlying process. Although the suitability of the approximation may significantly affect the estimation quality, to the effect of available approaches has not been paid a great interest in the literature. The aim of this contribution is the comparison of available sampling schemes and the assessment of sensitivity of existing maxima estimates of the extremal index.

English abstract

Extremal index is the primary measure of local dependence of extreme values, and plays thus an important role in extreme value estimation for stationary processes. The maxima estimators are often preferred in practical situations. These estimators are based on properties of the block maxima asymptotically characterized by the Generalized extreme value distribution. In contrast to the other methods, the maxima estimators gain advantage in stability to the choice of auxiliary parameters. Still the main part of maxima methods is selection of a proper approximation to marginal distribution of the underlying process. Although the suitability of the approximation may significantly affect the estimation quality, to the effect of available approaches has not been paid a great interest in the literature. The aim of this contribution is the comparison of available sampling schemes and the assessment of sensitivity of existing maxima estimates of the extremal index.

Keywords

extreme value; extremal index; stationary series; block maxima

Released

15.06.2017

Publisher

Univerzita obrany

Location

Brno

ISBN

978-80-7231-417-1

Book

Matematika, informační technologie a aplikované vědy, MITAV 2017

Pages from

1

Pages to

6

Pages count

6

BibTex


@inproceedings{BUT137200,
  author="Jan {Holešovský}",
  title="Sensitivity assessment of extremal index maxima estimates in the estimation of extreme values for stationary time series",
  annote="Extremal index is the primary measure of local dependence of extreme values, and plays thus an important role in extreme value estimation for stationary processes. The maxima estimators are often preferred in practical situations. These estimators are based on properties of the block maxima asymptotically characterized by the Generalized extreme value distribution. In contrast to the other methods, the maxima estimators gain advantage in stability to the choice of auxiliary parameters. Still the main part of maxima methods is selection of a proper approximation to marginal distribution of the underlying process. Although the suitability of the approximation may significantly affect the estimation quality, to the effect of available approaches has not been paid a great interest in the literature. The aim of this contribution is the comparison of available sampling schemes and the assessment of sensitivity of existing maxima estimates of the extremal index.",
  address="Univerzita obrany",
  booktitle="Matematika, informační technologie a aplikované vědy, MITAV 2017",
  chapter="137200",
  howpublished="electronic, physical medium",
  institution="Univerzita obrany",
  year="2017",
  month="june",
  pages="1--6",
  publisher="Univerzita obrany",
  type="conference paper"
}