Minimum Distance Index for BSS, Generalization, Interpretation and Asymptotics

  • Niko Lietzén Aalto University, School of Science
  • Joni Virta Aalto University, School of Science; University of Turku
  • Klaus Nordhausen Institute of Statistics & Mathematical Methods in Economics, Vienna University of Technology
  • Pauliina Ilmonen Aalto University, School of Science

Abstract

We consider complex valued linear blind source separation, where the signal dimension might be smaller than the dimension of the observable data vector. In order to measure the success of the signal separation, we propose an extension of the minimum distance index and establish its properties. Interpretations for the index are derived through connections to signal-to-noise ratios and correlations. The interpretations are novel also for the real valued original case. In addition, we consider the asymptotic behavior of the extended minimum distance index. This paper is an invited extended version of the paper presented at the CDAM 2019 conference.

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Published
2020-04-13
How to Cite
Lietzén, N., Virta, J., Nordhausen, K., & Ilmonen, P. (2020). Minimum Distance Index for BSS, Generalization, Interpretation and Asymptotics. Austrian Journal of Statistics, 49(4), 57-68. https://doi.org/10.17713/ajs.v49i4.1130
Section
Special Issue CDAM conference