Non-Gaussian component analysis: testing the dimension of the signal subspace
Methodology
2020-09-01 v1
Abstract
Dimension reduction is a common strategy in multivariate data analysis which seeks a subspace which contains all interesting features needed for the subsequent analysis. Non-Gaussian component analysis attempts for this purpose to divide the data into a non-Gaussian part, the signal, and a Gaussian part, the noise. We will show that the simultaneous use of two scatter functionals can be used for this purpose and suggest a bootstrap test to test the dimension of the non-Gaussian subspace. Sequential application of the test can then for example be used to estimate the signal dimension.
Keywords
Cite
@article{arxiv.2007.02751,
title = {Non-Gaussian component analysis: testing the dimension of the signal subspace},
author = {Una Radojicic and Klaus Nordhausen},
journal= {arXiv preprint arXiv:2007.02751},
year = {2020}
}