paper | Klaus Nordhausen

Posts tagged with: paper

Jan 25, 2017 |

New paper “Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace” on ArXiv

Our recent manuscript “Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace” is on arXiv. The methods described there are also implemented in our R-package ICtest.

Jan 12, 2017 |

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“Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp” has appeared

Our paper “Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp” has appeared in the Journal of Statistical Software!

Along with it come new version of our R packages JADE and BSSasymp which are both on CRAN.

BSS ICA paper R

Dec 20, 2016 |

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End of the year rush

The end of 2016 was busy but productive. Two more papers were uploaded to Arxiv:

and two R packages were uploaded to CRAN:

ICSOutlier which implements the methods from paper 2 above.
ICtest which provides the methods from paper 1 above and from our recently submitted paper “Asymptotic and bootstrap tests for subspace dimension“.

Feedback and comments are welcome!

bootstrap ICA ICS outlier paper R

Nov 23, 2016 |

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“Complex valued robust multidimensional SOBI” was accepted for LVA/ICA 2017

Also our second submission “Complex values robust multidimensional SOBI” was accepted for the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation – LVA/ICA 2017.

BSS ICA paper robust

Nov 22, 2016 |

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“On the Optimal Non-linearities in FastICA” was accepted for LVA/ICA 2017

Our paper “On the Optimal Non-linearities in FastICA” was accepted for the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation – LVA/ICA 2017.

ICA paper

Nov 17, 2016 |

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New paper “Asymptotic and bootstrap tests for subspace dimension” on arXiv

Our new manuscript “Asymptotic and bootstrap tests for subspace dimension” is available on Arxiv .

bootstrap ICA ICS paper SICS SIR

Oct 30, 2016 |

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“Applying fully tensorial ICA to fMRI data” was accepted for SPMB16

Our paper “Applying fully tensorial ICA to fMRI data” was accepted for the proceedings for the IEEE Signal Processing in Medicine and Biology Symposium (SPMB16).

paper tensor