Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace
Statistics Theory
2023-07-19 v1 Statistics Theory
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 paper we develop asymptotic as well as bootstrap tests for the dimension based on the popular fourth order blind identification (FOBI) method.
Keywords
Cite
@article{arxiv.1701.06836,
title = {Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace},
author = {Klaus Nordhausen and Hannu Oja and David E. Tyler and Joni Virta},
journal= {arXiv preprint arXiv:1701.06836},
year = {2023}
}