Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)
Asymptotic and Bootstrap Tests for the Dimension of the Non-Gaussian Subspace
Julkaisun tekijät: Nordhausen K, Oja H, Tyler DE, Virta J
Kustantaja: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Julkaisuvuosi: 2017
Journal: IEEE Signal Processing Letters
Tietokannassa oleva lehden nimi: IEEE SIGNAL PROCESSING LETTERS
Lehden akronyymi: IEEE SIGNAL PROC LET
Volyymi: 24
Julkaisunumero: 6
Aloitussivu: 887
Lopetussivun numero: 891
Sivujen määrä: 5
ISSN: 1070-9908
eISSN: 1558-2361
DOI: http://dx.doi.org/10.1109/LSP.2017.2696880
Rinnakkaistallenteen osoite: https://arxiv.org/abs/1701.06836
Tiivistelmä
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.
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.
Turun yliopiston Tutkimustietojärjestelmän portaali