A4 Artikkeli konferenssijulkaisussa
Signal dimension estimation in BSS models with serial dependence (2022)
Nordhausen, K., Taskinen, S., & Virta, J. (2022). Signal dimension estimation in BSS models with serial dependence. In ICECCME 2022 : Proceedings of the 2nd International Conference on Electrical, Computer, Communications and Mechatronics Engineering. IEEE. https://doi.org/10.1109/ICECCME55909.2022.9988152
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Nordhausen, Klaus; Taskinen, Sara; Virta, Joni
Emojulkaisu: ICECCME 2022 : Proceedings of the 2nd International Conference on Electrical, Computer, Communications and Mechatronics Engineering
Konferenssi:
- International Conference on Electrical, Computer, Communications and Mechatronics Engineering
Konferenssin paikka ja aika: Male, Maldives, 16.-18.11.2022
ISBN: 978-1-6654-7096-4
eISBN: 978-1-6654-7095-7
Julkaisuvuosi: 2022
Ilmestymispäivä: 30.12.2022
Kustantaja: IEEE
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1109/ICECCME55909.2022.9988152
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/85279
Tiivistelmä
Many modern multivariate time series datasets contain a large amount of noise, and the first step of the data analysis is to separate the noise channels from the signals of interest. A crucial part of this dimension reduction is determining the number of signals. In this paper we approach this problem by considering a noisy latent variable time series model which comprises many popular blind source separation models. We propose a general framework for the estimation of the signal dimension that is based on testing for sub-sphericity and give examples of different tests suitable for time series settings. In the inference we rely on bootstrap null distributions. Several simulation studies are used to demonstrate the performances of the tests in different time series settings.
YSO-asiasanat: aikasarjat; aikasarja-analyysi; signaalinkäsittely
Vapaat asiasanat: dimension reduction; nonstationary source separation; second order source separation; sub-sphericity; block bootstrap
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2022
JUFO-taso: 1
