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Large sample correlation matrices with unbounded spectrum

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  • Li, Yanpeng

Abstract

In this paper, we demonstrate that the diagonal of a high-dimensional sample covariance matrix stemming from n independent observations of a p-dimensional time series with finite fourth moments can be approximated in spectral norm by the diagonal of the population covariance matrix regardless of the spectral norm of the population covariance matrix. Our assumptions involve p and n tending to infinity, with p/n tending to a constant which might be positive or zero. Consequently, we investigate the asymptotic properties of the sample correlation matrix with a divergent spectrum, and we explore its applications by deriving the limiting spectral distribution for its eigenvalues and analyzing the convergence of divergent and non-divergent spiked eigenvalues under a generalized spiked correlation framework.

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  • Li, Yanpeng, 2025. "Large sample correlation matrices with unbounded spectrum," Journal of Multivariate Analysis, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:jmvana:v:205:y:2025:i:c:s0047259x24000800
    DOI: 10.1016/j.jmva.2024.105373
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    References listed on IDEAS

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