A1 Refereed original research article in a scientific journal

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



Authors: Nordhausen K, Oja H, Tyler DE, Virta J

Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Publication year: 2017

Journal:: IEEE Signal Processing Letters

Journal name in source: IEEE SIGNAL PROCESSING LETTERS

Journal acronym: IEEE SIGNAL PROC LET

Volume: 24

Issue: 6

First page : 887

Last page: 891

Number of pages: 5

ISSN: 1070-9908

eISSN: 1558-2361

DOI: https://doi.org/10.1109/LSP.2017.2696880

Self-archived copy’s web address: https://arxiv.org/abs/1701.06836


Abstract
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.



Last updated on 2024-26-11 at 17:06