Regularized covariance matrix estimation in high dimensional approximate factor models
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Publication:6540874
DOI10.1016/J.SPL.2023.110017zbMATH Open1537.62022MaRDI QIDQ6540874
Publication date: 17 May 2024
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07)
Cites Work
- Title not available (Why is that?)
- High-dimensional covariance matrix estimation in approximate factor models
- A Bernstein type inequality and moderate deviations for weakly dependent sequences
- Robust covariance estimation for approximate factor models
- Error covariance matrix estimation using ridge estimator
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- Consistent estimation of high-dimensional factor models when the factor number is over-estimated
- A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
- Large-Dimensional Factor Analysis Without Moment Constraints
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