A more efficient second order blind identification method for separation of uncorrelated stationary time series
Taskinen, S., Miettinen, J., & Nordhausen, K. (2016). A more efficient second order blind identification method for separation of uncorrelated stationary time series. Statistics and Probability Letters, 116, 21-26. https://doi.org/10.1016/j.spl.2016.04.007
Published inStatistics and Probability Letters
© 2016 Elsevier B.V. This is a final draft version of an article whose final and definitive form has been published by Elsevier. Published in this repository with the kind permission of the publisher.
The classical second order source separation methods use approximate joint diagonalization of autocovariance matrices with several lags to estimate the unmixing matrix. Based on recent asymptotic results, we propose a novel unmixing matrix estimator which selects the best lag set from a finite set of candidate sets specified by the user. The theory is illustrated by a simulation study.
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