Asymptotic and Bootstrap Tests for the Dimension of the Non-Gaussian Subspace

Klaus Nordhausen, Hannu Oja, David E. Tyler, Joni Virta

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Dimension reduction is often a preliminary step in the analysis of large data sets. The so-called non-Gaussian component analysis searches for a projection onto the non-Gaussian part of the data, and it is then important to know the correct dimension of the non-Gaussian signal subspace. In this letter, we develop asymptotic as well as bootstrap tests for the dimension based on the popular fourth-order blind identification method.

Original languageEnglish (US)
Article number7907304
Pages (from-to)887-891
Number of pages5
JournalIEEE Signal Processing Letters
Issue number6
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Fourth-order blind identification (FOBI)
  • independent component analysis (ICA)
  • non-Gaussian component analysis (NGCA)

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