Spectral statistics of high dimensional sample covariance matrix with unbounded population spectral norm

DOI10.3150/21-BEJ1391MaRDI QIDQ2137038

Yanqing Yin

Publication date: 16 May 2022

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.03417

zbMATH Keywords

large covariance matrixlinear spectral statisticsspiked eigenvalueunbounded spectral norm

Mathematics Subject Classification ID

Multivariate analysis (62Hxx) Limit theorems in probability theory (60Fxx) Special matrices (15Bxx)

Related Items (1)

A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes

Cites Work

• Unnamed Item
• Central limit theorem for signal-to-interference ratio of reduced rank linear receiver
• Spectral analysis of large dimensional random matrices
• Some limit theorems for the eigenvalues of a sample covariance matrix
• High-dimensional covariance matrices in elliptical distributions with application to spherical test
• On the distribution of the largest eigenvalue in principal components analysis
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• CLT for linear spectral statistics of large-dimensional sample covariance matrices.
• Independence test for high dimensional data based on regularized canonical correlation coefficients
• Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing
• Testing for complete independence in high dimensions
• High Dimensional Correlation Matrices: The Central Limit Theorem and Its Applications
• Asymptotic power of the sphericity test under weak and strong factors in a fixed effects panel data model

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