Hypothesis Testing for Block-structured Correlation for High Dimensional Variables
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Publication:5066769
DOI10.5705/ss.202019.0319OpenAlexW3173674205MaRDI QIDQ5066769
Xuming He, Shurong Zheng, Jian-hua Guo
Publication date: 30 March 2022
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.5705/ss.202019.0319
multivariate statistical analysishigh-dimensionsparse alternativesnon-sparse alternativestesting block-independence
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- On high-dimensional sign tests
- Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
- Testing block-diagonal covariance structure for high-dimensional data under non-normality
- Test of independence for high-dimensional random vectors based on freeness in block correlation matrices
- Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices
- Spectral analysis of large dimensional random matrices
- Corrections to LRT on large-dimensional covariance matrix by RMT
- Empirical Bayes estimation of the multivariate normal covariance matrix
- CLT for linear spectral statistics of large-dimensional sample covariance matrices.
- The asymptotic distributions of the largest entries of sample correlation matrices.
- Testing the independence of sets of large-dimensional variables
- Asymptotic theory for maximum deviations of sample covariance matrix estimates
- Forward Regression for Ultra-High Dimensional Variable Screening
- Testing for complete independence in high dimensions
- Asymptotic distribution of the largest off-diagonal entry of correlation matrices
- Power Enhancement in High-Dimensional Cross-Sectional Tests
- On the Independence of k Sets of Normally Distributed Statistical Variables
- Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
- DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
- Asymptotic Expansions of the Non-Null Distributions of the Likelihood Ratio Criteria for Multivariate Linear Hypothesis and Independence