• Corpus ID: 88517109

# Spiked covariances and principal components analysis in high-dimensional random effects models

@article{Fan2018SpikedCA,
title={Spiked covariances and principal components analysis in high-dimensional random effects models},
author={Zhou Fan and Iain M. Johnstone and Yi Sun},
journal={arXiv: Statistics Theory},
year={2018}
}
• Published 25 June 2018
• Mathematics
• arXiv: Statistics Theory
We study principal components analyses in multivariate random and mixed effects linear models, assuming a spherical-plus-spikes structure for the covariance matrix of each random effect. We characterize the behavior of outlier sample eigenvalues and eigenvectors of MANOVA variance components estimators in such models under a high-dimensional asymptotic regime. Our results show that an aliasing phenomenon may occur in high dimensions, in which eigenvalues and eigenvectors of the MANOVA estimate…

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