Projection tests for high-dimensional spiked covariance matrices
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Publication:1755108
DOI10.1016/j.jmva.2018.08.009zbMath1411.62149OpenAlexW2889348644WikidataQ129336306 ScholiaQ129336306MaRDI QIDQ1755108
Publication date: 4 January 2019
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2018.08.009
Multivariate distribution of statistics (62H10) Factor analysis and principal components; correspondence analysis (62H25) Hypothesis testing in multivariate analysis (62H15)
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- Two sample tests for high-dimensional covariance matrices
- More powerful tests for sparse high-dimensional covariances matrices
- Asymptotic power of sphericity tests for high-dimensional data
- Optimal detection of sparse principal components in high dimension
- Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
- Tests for covariance matrix with fixed or divergent dimension
- High-dimensional variable selection
- Covariance regularization by thresholding
- Corrections to LRT on large-dimensional covariance matrix by RMT
- 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
- Optimal hypothesis testing for high dimensional covariance matrices
- Sparse PCA: optimal rates and adaptive estimation
- p-Values for High-Dimensional Regression
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Testing Hypotheses About the Number of Factors in Large Factor Models
- Power Enhancement in High-Dimensional Cross-Sectional Tests
- Tests for High-Dimensional Covariance Matrices
- Performance of Statistical Tests for Single-Source Detection Using Random Matrix Theory
- Roy’s largest root test under rank-one alternatives
