All parameters in linear simultaneous equations models can be identified (up to permutation
and sign) if the underlying structural shocks are independent and at most one of them is …

We extend two methods of independent component analysis, fourth order blind identification
and joint approximate diagonalization of eigen-matrices, to vector-valued functional data …

Independent component analysis (ICA) is widely used to estimate spatial resting-state
networks and their time courses in neuroimaging studies. It is thought that independent …

Independent component analysis is commonly applied to functional magnetic resonance
imaging (fMRI) data to extract independent components (ICs) representing functional brain …

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 …

All parameters in linear simultaneous equations models can be identified (up to permutation
and scale) if the underlying structural shocks are independent and if at most one of them is …

Simultaneous non-Gaussian component analysis (SING) for data integration in
neuroimaging Page 1 The Annals of Applied Statistics 2021, Vol. 15, No. 3, 1431–1454 https://doi.org/10.1214/21-AOAS1466 …

Independent component analysis (ICA) decomposes multivariate data into mutually
independent components (ICs). The ICA model is subject to a constraint that at most one of …

We apply both distance-based (Jin and Matteson, 2017) and kernel-based (Pfister et al.,
2016) mutual dependence measures to independent component analysis (ICA), and …

This paper introduces an R package that implements Simultaneous non-Gaussian
Component Analysis for data integration. SING uses a non-Gaussian measure of information …