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A more efficient second order blind identification method for separation of uncorrelated stationary time series

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  • Taskinen, Sara
  • Miettinen, Jari
  • Nordhausen, Klaus

Abstract

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.

Suggested Citation

  • Taskinen, Sara & Miettinen, Jari & Nordhausen, Klaus, 2016. "A more efficient second order blind identification method for separation of uncorrelated stationary time series," Statistics & Probability Letters, Elsevier, vol. 116(C), pages 21-26.
    • Handle: RePEc:eee:stapro:v:116:y:2016:i:c:p:21-26
    DOI: 10.1016/j.spl.2016.04.007
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    References listed on IDEAS

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    1. Douglas B. Clarkson, 1988. "A Least Squares Version of Algorithm as 211: The F‐G Diagonalization Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(2), pages 317-321, June.
    2. Miettinen, Jari & Nordhausen, Klaus & Oja, Hannu & Taskinen, Sara, 2014. "Deflation-based separation of uncorrelated stationary time series," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 214-227.
    3. Jari Miettinen & Katrin Illner & Klaus Nordhausen & Hannu Oja & Sara Taskinen & Fabian J. Theis, 2016. "Separation of Uncorrelated Stationary time series using Autocovariance Matrices," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 337-354, May.
    4. Miettinen, Jari & Nordhausen, Klaus & Oja, Hannu & Taskinen, Sara, 2012. "Statistical properties of a blind source separation estimator for stationary time series," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1865-1873.
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    Cited by:

    1. Jari Miettinen & Markus Matilainen & Klaus Nordhausen & Sara Taskinen, 2020. "Extracting Conditionally Heteroskedastic Components using Independent Component Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 293-311, March.
    2. Nordhausen, Klaus & Ruiz-Gazen, Anne, 2021. "On the usage of joint diagonalization in multivariate statistics," TSE Working Papers 21-1268, Toulouse School of Economics (TSE).
    3. Nordhausen, Klaus & Ruiz-Gazen, Anne, 2022. "On the usage of joint diagonalization in multivariate statistics," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    4. Matilainen, M. & Croux, C. & Nordhausen, K. & Oja, H., 2017. "Supervised dimension reduction for multivariate time series," Econometrics and Statistics, Elsevier, vol. 4(C), pages 57-69.
    5. Klaus Nordhausen & Anne Ruiz-Gazen, 2022. "On the usage of joint diagonalization in multivariate statistics," Post-Print hal-04296111, HAL.

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