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Separation of uncorrelated stationary time series using autocovariance matrices.

J. Time Ser. Anal. 37, 337-354 (2016)
Postprint DOI
Open Access Green
as soon as is submitted to ZB.
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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Publication type Article: Journal article
Document type Scientific Article
Keywords Asymptotic Normality ; Blind Source Separation ; Joint Diagonalization ; Linear Process ; Sobi; Independent Component Analysis; Blind Source Separation; Factor Models; Distributions
Reviewing status