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.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics
- Fourth-order blind identification (FOBI)
- independent component analysis (ICA)
- non-Gaussian component analysis (NGCA)