Principal components in linear mixed models with general bulk
Abstract
We study the principal components of covariance estimators in multivariate mixed-effects linear models. We show that, in high dimensions, the principal eigenvalues and eigenvectors may exhibit bias and aliasing effects that are not present in low-dimensional settings. We derive the first-order limits of the principal eigenvalue locations and eigenvector projections in a high-dimensional asymptotic framework, allowing for general population spectral distributions for the random effects and extending previous results from a more restrictive spiked model. Our analysis uses free probability techniques, and we develop two general tools of independent interest-- strong asymptotic freeness of GOE and deterministic matrices and a free deterministic equivalent approximation for bilinear forms of resolvents.
Cite
@article{arxiv.1903.09592,
title = {Principal components in linear mixed models with general bulk},
author = {Zhou Fan and Yi Sun and Zhichao Wang},
journal= {arXiv preprint arXiv:1903.09592},
year = {2020}
}