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Blind Source Separation over Space: an eigenanalysis approach

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  • Zhang, Bo
  • Hao, Sixing
  • Yao, Qiwei

Abstract

We propose a new estimation method for the blind source separation model of Bachoc et al. (2020). The new estimation is based on an eigenanalysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and, therefore, can handle moderately high-dimensional random fields. The consistency of the estimated mixing matrix is established with explicit error rates even when the eigen-gap decays to zero slowly. The proposed method is illustrated via both simulation and a real data example.

Suggested Citation

  • Zhang, Bo & Hao, Sixing & Yao, Qiwei, 2023. "Blind Source Separation over Space: an eigenanalysis approach," LSE Research Online Documents on Economics 121093, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:121093
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    File URL: http://eprints.lse.ac.uk/121093/
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    References listed on IDEAS

    as
    1. François Bachoc & Marc G Genton & Klaus Nordhausen & Anne Ruiz-Gazen & Joni Virta, 2020. "Spatial blind source separation," Biometrika, Biometrika Trust, vol. 107(3), pages 627-646.
    2. Bachoc, François, 2014. "Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 1-35.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Eigen-analysis; Eigen-gap; high-dimensional random field; mixing matrix; normalized spatial local covariance matrix.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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