Asymptotic and Bootstrap Tests for the Dimension of the Non-Gaussian Subspace
Klaus Nordhausen, Hannu Oja, David E. Tyler, Joni Virta · IEEE Signal Processing Letters · 2017
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.