Matrix analysis
Limiting spectral distribution for a class of random matrices
Journal of Multivariate Analysis
On the empirical distribution of eigenvalues of a class of large dimensional random matrices
Journal of Multivariate Analysis
Analysis of the limiting spectral distribution of large dimensional random matrices
Journal of Multivariate Analysis
Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
Journal of Multivariate Analysis
Journal of Multivariate Analysis
Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in Wishart matrices
IEEE Communications Letters
Non-parametric detection of the number of signals: hypothesis testing and random matrix theory
IEEE Transactions on Signal Processing
Singular value decomposition of large random matrices (for two-way classification of microarrays)
Journal of Multivariate Analysis
Journal of Multivariate Analysis
A Rotation Test to Verify Latent Structure
The Journal of Machine Learning Research
Minimax rates of convergence for high-dimensional regression under lq-ball sparsity
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Journal of Multivariate Analysis
Journal of Multivariate Analysis
On sample eigenvalues in a generalized spiked population model
Journal of Multivariate Analysis
On the border of extreme and mild spiked models in the HDLSS framework
Journal of Multivariate Analysis
A theoretical investigation of several model selection criteria for dimensionality reduction
Pattern Recognition Letters
Boundary behavior in High Dimension, Low Sample Size asymptotics of PCA
Journal of Multivariate Analysis
The singular values and vectors of low rank perturbations of large rectangular random matrices
Journal of Multivariate Analysis
A subspace estimator for fixed rank perturbations of large random matrices
Journal of Multivariate Analysis
Correlation tests for high-dimensional data using extended cross-data-matrix methodology
Journal of Multivariate Analysis
Learning a factor model via regularized PCA
Machine Learning
PCA consistency for the power spiked model in high-dimensional settings
Journal of Multivariate Analysis
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We consider a spiked population model, proposed by Johnstone, in which all the population eigenvalues are one except for a few fixed eigenvalues. The question is to determine how the sample eigenvalues depend on the non-unit population ones when both sample size and population size become large. This paper completely determines the almost sure limits of the sample eigenvalues in a spiked model for a general class of samples.