On the eigenstructure of covariance matrices with divergent spikes

DOI10.3150/22-BEJ1498MaRDI QIDQ2692533

Simona Diaconu

Publication date: 22 March 2023

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2112.07591

zbMATH Keywords

covariance matricesdivergent spikeseigenvalue CLTs

Mathematics Subject Classification ID

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

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Eigenvectors of some large sample covariance matrix ensembles
• Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
• Spectral analysis of large dimensional random matrices
• PCA consistency in high dimension, low sample size context
• On the distribution of the largest eigenvalue in principal components analysis
• On the empirical distribution of eigenvalues of a class of large dimensional random matrices
• Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
• Asymptotics of empirical eigenstructure for high dimensional spiked covariance
• Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices
• Eigenvalues of large sample covariance matrices of spiked population models
• A General Framework For Consistency of Principal Component Analysis
• High-Dimensional Probability
• Geometric Representation of High Dimension, Low Sample Size Data
• The high-dimension, low-sample-size geometric representation holds under mild conditions
• Asymptotic Theory for Principal Component Analysis

This page was built for publication: On the eigenstructure of covariance matrices with divergent spikes