Testing for the expected number of exceedances in strongly dependent seasonal time series

Beran, Jan; Steffens, Britta; Ghosh, Sucharita

Testing for the expected number of exceedances in strongly dependent seasonal time series

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2021

Autor:innen

Beran, Jan

Steffens, Britta

Ghosh, Sucharita

DOI (zitierfähiger Link)

https://doi.org/10.1080/10485252.2021.1977301

Sammlungen

Mathematik und Statistik

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Journal of Nonparametric Statistics. Taylor & Francis. 2021, 33(3-4), pp. 417-434. ISSN 1048-5252. eISSN 1029-0311. Available under: doi: 10.1080/10485252.2021.1977301

Zusammenfassung

We consider seasonal time series models with a strongly dependent residual process. The question of testing for a change in the expected number of exceedances is addressed. Based on a functional limit theorem for seasonal empirical processes, a test of the null hypothesis of no change is proposed. The method is applied to daily temperature series at various locations in Switzerland. The test reveals interesting differences in the effect of global warming on seasonal temperature exceedances.

Fachgebiet (DDC)

510 Mathematik

Zitieren

ISO 690

BERAN, Jan, Britta STEFFENS, Sucharita GHOSH, 2021. Testing for the expected number of exceedances in strongly dependent seasonal time series. In: Journal of Nonparametric Statistics. Taylor & Francis. 2021, 33(3-4), pp. 417-434. ISSN 1048-5252. eISSN 1029-0311. Available under: doi: 10.1080/10485252.2021.1977301

BibTex

@article{Beran2021Testi-55173,
  year={2021},
  doi={10.1080/10485252.2021.1977301},
  title={Testing for the expected number of exceedances in strongly dependent seasonal time series},
  number={3-4},
  volume={33},
  issn={1048-5252},
  journal={Journal of Nonparametric Statistics},
  pages={417--434},
  author={Beran, Jan and Steffens, Britta and Ghosh, Sucharita}
}

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