Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace

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.

Cited by in corpus (9)

• ICS for Multivariate Outlier Detection with Application to Quality Control
• Independent component analysis for tensor-valued data
• Asymptotic and bootstrap tests for subspace dimension
• On the number of signals in multivariate time series
• Numerical considerations and a new implementation for ICS
• Tandem clustering with invariant coordinate selection
• Stationary subspace analysis based on second-order statistics
• Non-Gaussian component analysis: testing the dimension of the signal subspace
• Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals