Deprecated: $wgMWOAuthSharedUserIDs=false is deprecated, set $wgMWOAuthSharedUserIDs=true, $wgMWOAuthSharedUserSource='local' instead [Called from MediaWiki\HookContainer\HookContainer::run in /var/www/html/w/includes/HookContainer/HookContainer.php at line 135] in /var/www/html/w/includes/Debug/MWDebug.php on line 372
On asymptotics of eigenvectors of large sample covariance matrix - MaRDI portal

On asymptotics of eigenvectors of large sample covariance matrix

From MaRDI portal
Publication:2373573

DOI10.1214/009117906000001079zbMath1162.15012arXiv0708.1720OpenAlexW3104309687MaRDI QIDQ2373573

Guangming Pan, Baiqi Miao, Zhi-Dong Bai

Publication date: 12 July 2007

Published in: The Annals of Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0708.1720



Related Items

Direct shrinkage estimation of large dimensional precision matrix, Spectral analysis of the Moore-Penrose inverse of a large dimensional sample covariance matrix, A family of flexible shrinkage estimators for the variances of high-dimensional gene expressions, The spectrum of kernel random matrices, Nonstandard limit theorems and large deviations for the Jacobi beta ensemble, Statistical Inference for High-Dimensional Global Minimum Variance Portfolios, A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data, Canonical moments and random spectral measures, Asymptotics of AIC, BIC and \(C_p\) model selection rules in high-dimensional regression, Inference for the Dimension of a Regression Relationship Using Pseudo-Covariates, Universality for Eigenvalue Algorithms on Sample Covariance Matrices, Convergence rate of eigenvector empirical spectral distribution of large Wigner matrices, Convergence of eigenvector empirical spectral distribution of sample covariance matrices, The eigenvector LSD of information plus noise matrices and its application to linear regression model, On bilinear forms based on the resolvent of large random matrices, A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes, Spectral convergence for a general class of random matrices, Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices, Eigenvector delocalization for non‐Hermitian random matrices and applications, Most powerful test against a sequence of high dimensional local alternatives, Convergence rates of eigenvector empirical spectral distribution of large dimensional sample covariance matrix, Eigenvectors of some large sample covariance matrix ensembles, On the strong convergence of the optimal linear shrinkage estimator for large dimensional covariance matrix, Operator-valued spectral measures and large deviations, Central limit theorem for Hotelling's \(T^{2}\) statistic under large dimension, Asymptotic properties of eigenmatrices of a large sample covariance matrix, Comparison between two types of large sample covariance matrices, Central limit theorem for signal-to-interference ratio of reduced rank linear receiver, Random matrix theory in statistics: a review, The conjugate gradient algorithm on well-conditioned Wishart matrices is almost deterministic, Sparse regular random graphs: spectral density and eigenvectors, Functional CLT of eigenvectors for large sample covariance matrices, Estimation for biased partial linear single index models, On the dimension effect of regularized linear discriminant analysis, Spectral measures of spiked random matrices, High-Dimensional CLTs for Individual Mahalanobis Distances, Singular vector distribution of sample covariance matrices, Eigenvectors and controllability of non-Hermitian random matrices and directed graphs, Recent advances in shrinkage-based high-dimensional inference, ENHANCEMENT OF THE APPLICABILITY OF MARKOWITZ'S PORTFOLIO OPTIMIZATION BY UTILIZING RANDOM MATRIX THEORY, Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices, On the eigenvectors of large-dimensional sample spatial sign covariance matrices, CLT for spiked eigenvalues of a sample covariance matrix from high-dimensional Gaussian mean mixtures, Properties of eigenvalues and eigenvectors of large-dimensional sample correlation matrices, The conjugate gradient algorithm on a general class of spiked covariance matrices



Cites Work