Principal components in linear mixed models with general bulk
Zhou Fan, Yi Sun, Zhichao Wang
The Annals of Statistics · 2021 · Volume 49 · Issue 3
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.
Keywords
62E20, Free probability, high-dimensional asymptotics, random effects models, Random matrix theory
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