Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models
From MaRDI portal
Publication:5004034
DOI10.5705/ss.202019.0052zbMath1469.62267arXiv1810.10214OpenAlexW3024509615MaRDI QIDQ5004034
Iain M. Johnstone, Jeha Yang, David Morales-Jimenez, Matthew R. McKay
Publication date: 30 July 2021
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.10214
Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20) Random matrices (probabilistic aspects) (60B20)
Related Items (11)
Statistical inference for principal components of spiked covariance matrices ⋮ CLT for linear spectral statistics of high-dimensional sample covariance matrices in elliptical distributions ⋮ Principal Eigenportfolios for U.S. Equities ⋮ The volume of random simplices from elliptical distributions in high dimension ⋮ Robust PCA for high‐dimensional data based on characteristic transformation ⋮ On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence ⋮ Spiked multiplicative random matrices and principal components ⋮ Central limit theorem of linear spectral statistics of high-dimensional sample correlation matrices ⋮ On eigenvalues of a high-dimensional spatial-sign covariance matrix ⋮ Properties of eigenvalues and eigenvectors of large-dimensional sample correlation matrices ⋮ A Dichotomous Behavior of Guttman-Kaiser Criterion from Equi-Correlated Normal Population
Cites Work
- Unnamed Item
- Unnamed Item
- Phase transition in limiting distributions of coherence of high-dimensional random matrices
- Tracy-Widom law for the extreme eigenvalues of sample correlation matrices
- The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
- Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices
- Edge universality of correlation matrices
- Central limit theorems for eigenvalues in a spiked population model
- Almost sure limit of the smallest eigenvalue of some sample correlation matrices
- Concentration of measure and spectra of random matrices: applications to correlation matrices, elliptical distributions and beyond
- Asymptotic expansions for the distributions of statistics based on the sample correlation matrix in principal component analysis
- Asymptotic distributions of functions of the eigenvalues of some random matrices for nonnormal populations
- Asymptotics of eigenvalues and unit-length eigenvectors of sample variance and correlation matrices
- On the distribution of the largest eigenvalue in principal components analysis
- The asymptotic distributions of the largest entries of sample correlation matrices.
- A subspace estimator for fixed rank perturbations of large random matrices
- Eigenvalues of large sample covariance matrices of spiked population models
- Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
- On the principal components of sample covariance matrices
- Principal component models for correlation matrices
- Random Matrix Methods for Wireless Communications
- Fluctuations of Spiked Random Matrix Models and Failure Diagnosis in Sensor Networks
- Hub Discovery in Partial Correlation Graphs
- Large-Scale Correlation Screening
- A Test for a Specific Principal Component of a Correlation Matrix
- Performance Analysis of an Improved MUSIC DoA Estimator
- High-Dimensional MVDR Beamforming: Optimized Solutions Based on Spiked Random Matrix Models
- Large Sample Covariance Matrices and High-Dimensional Data Analysis
- Correlation Tests and Linear Spectral Statistics of the Sample Correlation Matrix
- High Dimensional Correlation Matrices: The Central Limit Theorem and Its Applications
- On the Sampling Theory of Roots of Determinantal Equations
This page was built for publication: Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models
