Eigenvalue Distribution of a High-Dimensional Distance Covariance Matrix With Application

DOI10.5705/ss.202020.0327arXiv2105.07641OpenAlexW3161409911WikidataQ114013822 ScholiaQ114013822MaRDI QIDQ6039863

Jian-feng Yao, Wei-Ming Li, Qinwen Wang

Publication date: 23 May 2023

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.07641

zbMATH Keywords

eigenvalue distributiondistance covariancespiked modelsfinite-rank perturbationnonlinear correlationdistance covariance matrix

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Measuring and testing dependence by correlation of distances
• The distance correlation \(t\)-test of independence in high dimension
• Identifying the number of factors from singular values of a large sample auto-covariance matrix
• Spectral analysis of large dimensional random matrices
• Canonical correlation coefficients of high-dimensional Gaussian vectors: finite rank case
• Distance-based and RKHS-based dependence metrics in high dimension
• Independence test for high dimensional data based on regularized canonical correlation coefficients
• Eigenvalues of large sample covariance matrices of spiked population models
• Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
• Unnamed Item

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