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Separation of uncorrelated stationary time series using autocovariance matrices.
J. Time Ser. Anal. 37, 337-354 (2016)
In blind source separation, one assumes that the observed p time series are linear combinations of p latent uncorrelated weakly stationary time series. To estimate the unmixing matrix, which transforms the observed time series back to uncorrelated latent time series, second-order blind identification (SOBI) uses joint diagonalization of the covariance matrix and autocovariance matrices with several lags. In this article, we find the limiting distribution of the well-known symmetric SOBI estimator under general conditions and compare its asymptotical efficiencies to those of the recently introduced deflation-based SOBI estimator. The theory is illustrated by some finite-sample simulation studies.
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Icb_biostatistics Icb_Latent Causes
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Asymptotic Normality ; Blind Source Separation ; Joint Diagonalization ; Linear Process ; Sobi; Independent Component Analysis; Blind Source Separation; Factor Models; Distributions
ISSN (print) / ISBN 0143-9782
e-ISSN 0143-9782
Zeitschrift Journal of Time Series Analysis
Quellenangaben Band: 37, Heft: 3, Seiten: 337-354 Artikelnummer: , Supplement: ,
Verlag Wiley-Blackwell
Verlagsort Oxford
Begutachtungsstatus peer-reviewed