Kunz, Pertami J. ; Zoubir, Abdelhak M. (2024)
Integrating Block Robust Empirical Standardised Influence Function and Block Average to Robustify Seasonal Block Bootstrap Methods.
32nd European Signal Processing Conference (EUSIPCO 2024). Lyon, France (26.08.2024 - 30.08.2024)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We propose two robust seasonal bootstrap techniques, based on our prior work, the Complex Seasonal Circular Block Bootstrap (XSCBB). These robustifications are implemented through two methods, the Block Robust Empirical Standardised Influence Function (BRESIF or Block RESIF) and the Block Average XSCBB (BA-XSCBB) methods. The BRESIF-XSCBB method employs an adaptive weighting function to adjust the XSCBB technique for time series with heavy-tailed distributions or extreme values. Conversely, the Block-Average/Median XSCBB strategy seeks to reduce the influence of outliers by incorporating either the block average or median to restore the seasonal characteristics. Through simulation studies, we showcase the efficacy of these methods in enhancing the XSCBB's performance when outliers are present. Our results indicate that these robustified versions of XSCBB not only pre-serve the original method's capacity to detect complex seasonal patterns but also significantly enhance resilience and accuracy in challenging data scenarios
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Kunz, Pertami J. ; Zoubir, Abdelhak M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Integrating Block Robust Empirical Standardised Influence Function and Block Average to Robustify Seasonal Block Bootstrap Methods |
| Sprache: | Englisch |
| Publikationsjahr: | 23 Oktober 2024 |
| Verlag: | IEEE |
| Buchtitel: | 32nd European Signal Processing Conference (EUSIPCO 2024): Proceedings |
| Veranstaltungstitel: | 32nd European Signal Processing Conference (EUSIPCO 2024) |
| Veranstaltungsort: | Lyon, France |
| Veranstaltungsdatum: | 26.08.2024 - 30.08.2024 |
| URL / URN: | https://ieeexplore.ieee.org/document/10715332 |
| Kurzbeschreibung (Abstract): | We propose two robust seasonal bootstrap techniques, based on our prior work, the Complex Seasonal Circular Block Bootstrap (XSCBB). These robustifications are implemented through two methods, the Block Robust Empirical Standardised Influence Function (BRESIF or Block RESIF) and the Block Average XSCBB (BA-XSCBB) methods. The BRESIF-XSCBB method employs an adaptive weighting function to adjust the XSCBB technique for time series with heavy-tailed distributions or extreme values. Conversely, the Block-Average/Median XSCBB strategy seeks to reduce the influence of outliers by incorporating either the block average or median to restore the seasonal characteristics. Through simulation studies, we showcase the efficacy of these methods in enhancing the XSCBB's performance when outliers are present. Our results indicate that these robustified versions of XSCBB not only pre-serve the original method's capacity to detect complex seasonal patterns but also significantly enhance resilience and accuracy in challenging data scenarios |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
| Hinterlegungsdatum: | 06 Nov 2024 15:39 |
| Letzte Änderung: | 06 Nov 2024 15:39 |
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