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On robustifying some second order blind source separation methods for nonstationary time series

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  • Klaus Nordhausen

Abstract

Blind source separation (BSS) is an important analysis tool in various signal processing applications like image, speech or medical signal analysis. The most popular BSS solutions have been developed for independent component analysis (ICA) with identically and independently distributed (iid) observation vectors. In many BSS applications the assumption on iid observations is not realistic, however, as the data are often an observed time series with temporal correlation and even nonstationarity. In this paper, some BSS methods for time series with nonstationary variances are discussed. We also suggest ways to robustify these methods and illustrate their performance in a simulation study. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Klaus Nordhausen, 2014. "On robustifying some second order blind source separation methods for nonstationary time series," Statistical Papers, Springer, vol. 55(1), pages 141-156, February.
    • Handle: RePEc:spr:stpapr:v:55:y:2014:i:1:p:141-156
    DOI: 10.1007/s00362-012-0487-5
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    References listed on IDEAS

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    1. Thomas P. Hettmansperger, 2002. "A practical affine equivariant multivariate median," Biometrika, Biometrika Trust, vol. 89(4), pages 851-860, December.
    2. Nordhausen, Klaus & Oja, Hannu & Tyler, David E., 2008. "Tools for Exploring Multivariate Data: The Package ICS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i06).
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    Cited by:

    1. 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.
    2. Nordhausen, Klaus & Ruiz-Gazen, Anne, 2022. "On the usage of joint diagonalization in multivariate statistics," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    3. 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.
    4. Miettinen, Jari & Nordhausen, Klaus & Taskinen, Sara, 2017. "Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i02).

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