Publikation: Testing for periodicity at an unknown frequency under cyclic long memory, with applications to respiratory muscle training
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2024
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AStA Advances in Statistical Analysis. Springer. 2024, 108(4), S. 705-731. ISSN 1863-8171. eISSN 1863-818X. Verfügbar unter: doi: 10.1007/s10182-024-00499-x
Zusammenfassung
A frequent problem in applied time series analysis is the identification of dominating periodic components. A particularly difficult task is to distinguish deterministic periodic signals from periodic long memory. In this paper, a family of test statistics based on Whittle’s Gaussian log-likelihood approximation is proposed. Asymptotic critical regions and bounds for the asymptotic power are derived. In cases where a deterministic periodic signal and periodic long memory share the same frequency, consistency and rates of type II error probabilities depend on the long-memory parameter. Simulations and an application to respiratory muscle training data illustrate the results.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
310 Statistik
Schlagwörter
Cyclic long memory, Periodicity, Deterministic periodicity, Periodogram, Gegenbauer process
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BERAN, Jan, Jeremy NÄSCHER, Fabian PIETSCH, Stephan WALTERSPACHER, 2024. Testing for periodicity at an unknown frequency under cyclic long memory, with applications to respiratory muscle training. In: AStA Advances in Statistical Analysis. Springer. 2024, 108(4), S. 705-731. ISSN 1863-8171. eISSN 1863-818X. Verfügbar unter: doi: 10.1007/s10182-024-00499-xBibTex
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title={Testing for periodicity at an unknown frequency under cyclic long memory, with applications to respiratory muscle training},
year={2024},
doi={10.1007/s10182-024-00499-x},
number={4},
volume={108},
issn={1863-8171},
journal={AStA Advances in Statistical Analysis},
pages={705--731},
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