Publikation: On randomly periodic strongly dependent time series, with applications to neural respiratory drive data
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2025
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Communications in Statistics: Theory and Methods. Taylor & Francis. 2025, 54(7), S. 2005-2032. ISSN 0361-0926. eISSN 1532-415X. Verfügbar unter: doi: 10.1080/03610926.2024.2355582
Zusammenfassung
We consider time series with a seasonal component that varies randomly in length and shape. The shape parameters of the seasonal process, as well as the noise component, are stationary and exhibit long-range dependence. A functional limit theorem for the estimated parameter process leads to asymptotic inference under suitable conditions on the observational grid. The model is motivated by a study of the effect of body positioning on respiratory muscles during weaning (Walterspacher et al. 2017).
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
510 Mathematik
Schlagwörter
Long memory, state space model, functional data analysis, mechanical ventilation, neural respiratory drive, surface electromyography (sEMG)
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BERAN, Jan, Jeremy NÄSCHER, Stephan WALTERSPACHER, 2025. On randomly periodic strongly dependent time series, with applications to neural respiratory drive data. In: Communications in Statistics: Theory and Methods. Taylor & Francis. 2025, 54(7), S. 2005-2032. ISSN 0361-0926. eISSN 1532-415X. Verfügbar unter: doi: 10.1080/03610926.2024.2355582BibTex
@article{Beran2025-04-03rando-70215,
title={On randomly periodic strongly dependent time series, with applications to neural respiratory drive data},
year={2025},
doi={10.1080/03610926.2024.2355582},
number={7},
volume={54},
issn={0361-0926},
journal={Communications in Statistics: Theory and Methods},
pages={2005--2032},
author={Beran, Jan and Näscher, Jeremy and Walterspacher, Stephan}
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