Publikation:

Estimating the Mean Direction of Strongly Dependent Circular Time Series

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Beran_2-qn2sncvrlmh14.pdf
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2020

Autor:innen

Ghosh, Sucharita

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http://nbn-resolving.de/urn:nbn:de:bsz:352-2-qn2sncvrlmh14
DOI (zitierfähiger Link)
https://doi.org/10.1111/jtsa.12500
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CC BY-NC-ND 4.0

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Mathematik und Statistik: Publikationen
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Published

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Journal of Time Series Analysis. Wiley. 2020, 41(2), pp. 210-228. ISSN 0143-9782. eISSN 1467-9892. Available under: doi: 10.1111/jtsa.12500

Zusammenfassung

A class of circular processes based on Gaussian subordination is introduced. This allows for flexible modelling of directional time series with long‐range dependence. Based on limit theorems for subordinated processes and consistent estimation of nuisance parameters, asymptotic confidence intervals for the mean direction are derived. Extensions to cases where the direction depends on explanatory variables are also considered. Simulations and a data example illustrate the proposed method.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
510 Mathematik

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ISO 690BERAN, Jan, Sucharita GHOSH, 2020. Estimating the Mean Direction of Strongly Dependent Circular Time Series. In: Journal of Time Series Analysis. Wiley. 2020, 41(2), pp. 210-228. ISSN 0143-9782. eISSN 1467-9892. Available under: doi: 10.1111/jtsa.12500
BibTex
@article{Beran2020-03Estim-46788,
  year={2020},
  doi={10.1111/jtsa.12500},
  title={Estimating the Mean Direction of Strongly Dependent Circular Time Series},
  number={2},
  volume={41},
  issn={0143-9782},
  journal={Journal of Time Series Analysis},
  pages={210--228},
  author={Beran, Jan and Ghosh, Sucharita}
}
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