Robust covariance estimation for approximate factor models
From MaRDI portal
Publication:1739628
DOI10.1016/j.jeconom.2018.09.003zbMath1452.62410arXiv1602.00719OpenAlexW2963840025WikidataQ64119840 ScholiaQ64119840MaRDI QIDQ1739628
Yiqiao Zhong, Jianqing Fan, Wei-Chen Wang
Publication date: 26 April 2019
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1602.00719
Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Robustness and adaptive procedures (parametric inference) (62F35)
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Uses Software
Cites Work
- Sparse inverse covariance estimation with the graphical lasso
- High dimensional covariance matrix estimation using a factor model
- Sparse principal component analysis and iterative thresholding
- Minimax bounds for sparse PCA with noisy high-dimensional data
- Optimal detection of sparse principal components in high dimension
- Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
- High-dimensional covariance matrix estimation in approximate factor models
- Risks of large portfolios
- Asymptotics of the principal components estimator of large factor models with weakly influential factors
- Optimal rates of convergence for covariance matrix estimation
- Covariance regularization by thresholding
- Sparsistency and rates of convergence in large covariance matrix estimation
- Challenging the empirical mean and empirical variance: a deviation study
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Asymptotics of empirical eigenstructure for high dimensional spiked covariance
- Optimal estimation and rank detection for sparse spiked covariance matrices
- Large covariance estimation through elliptical factor models
- Minimax sparse principal subspace estimation in high dimensions
- High-dimensional graphs and variable selection with the Lasso
- Exact matrix completion via convex optimization
- Robust principal component analysis?
- Rank-Sparsity Incoherence for Matrix Decomposition
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Asymptotically Minimax Adaptive Estimation. I: Upper Bounds. Optimally Adaptive Estimates
- Regularization of Wavelet Approximations
- Forecasting Using Principal Components From a Large Number of Predictors
- Scale-Invariant Sparse PCA on High-Dimensional Meta-Elliptical Data
- On Consistency and Sparsity for Principal Components Analysis in High Dimensions
- Generalized Thresholding of Large Covariance Matrices
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- Common risk factors in the returns on stocks and bonds
- Inferential Theory for Factor Models of Large Dimensions
- Robust Estimation of a Location Parameter
- Projected principal component analysis in factor models
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