Regularized LRT for large scale covariance matrices: one sample problem
DOI10.1016/j.jspi.2016.06.006zbMath1356.62070arXiv1502.00384OpenAlexW2120076219MaRDI QIDQ338414
Johan Lim, Chi Tim Ng, Young-Geun Choi
Publication date: 4 November 2016
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1502.00384
asymptotic normalityrandom matrix theoryhigh-dimensional datacovariance matrix estimatoridentity covariance matrixlinear shrinkage estimatorlinear spectral statisticsregularized likelihood ratio testspiked covariance matrix
Multivariate distribution of statistics (62H10) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Random matrices (algebraic aspects) (15B52)
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Cites Work
- Unnamed Item
- A well-conditioned estimator for large-dimensional covariance matrices
- Likelihood ratio tests for covariance matrices of high-dimensional normal distributions
- 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
- CLT for linear spectral statistics of large-dimensional sample covariance matrices.
- A note on the CLT of the LSS for sample covariance matrix from a spiked population model
- Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing
- A Regularized Hotelling’sT2Test for Pathway Analysis in Proteomic Studies
- Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices
- Tests for High-Dimensional Covariance Matrices
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