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
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Statistics and Probability LettersDate
2016Copyright
© 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.
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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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