Robust high-dimensional factor models with applications to statistical machine learning
From MaRDI portal
Publication:2038305
DOI10.1214/20-STS785MaRDI QIDQ2038305
Ziwei Zhu, Yiqiao Zhong, Kaizheng Wang, Jianqing Fan
Publication date: 6 July 2021
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1808.03889
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Cites Work
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- Fast community detection by SCORE
- High dimensional covariance matrix estimation using a factor model
- Covariance estimation for distributions with \({2+\varepsilon}\) moments
- Factor modeling for high-dimensional time series: inference for the number of factors
- High-dimensional covariance matrix estimation in approximate factor models
- Statistical analysis of factor models of high dimension
- Concentration inequalities and moment bounds for sample covariance operators
- Asymptotics of the principal components estimator of large factor models with weakly influential factors
- The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
- Spectral clustering and the high-dimensional stochastic blockmodel
- Factor models and variable selection in high-dimensional regression analysis
- User-friendly tail bounds for sums of random matrices
- How close is the sample covariance matrix to the actual covariance matrix?
- Eigenvectors of random matrices: A survey
- ``Preconditioning for feature selection and regression in high-dimensional problems
- Covariance regularization by thresholding
- The asymptotic normal distribution of estimators in factor analysis under general conditions
- Confounder adjustment in multiple hypothesis testing
- Random perturbation of low rank matrices: improving classical bounds
- Fundamental limits of symmetric low-rank matrix estimation
- Challenging the empirical mean and empirical variance: a deviation study
- Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries
- A rationale and test for the number of factors in factor analysis
- Asymptotics of empirical eigenstructure for high dimensional spiked covariance
- Entrywise eigenvector analysis of random matrices with low expected rank
- The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
- Simultaneous analysis of Lasso and Dantzig selector
- Large covariance estimation through elliptical factor models
- Optimal shrinkage of eigenvalues in the spiked covariance model
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
- Exact matrix completion via convex optimization
- Eigenvalue Ratio Test for the Number of Factors
- Tensor decompositions for learning latent variable models
- Computational Advertising: Techniques for Targeting Relevant Ads
- Perturbation of Linear Forms of Singular Vectors Under Gaussian Noise
- A general framework for multiple testing dependence
- Exact Recovery in the Stochastic Block Model
- Distributed 3-D Localization of Camera Sensor Networks From 2-D Image Measurements
- Learning mixtures of spherical gaussians
- Robust principal component analysis?
- A Factor Model Approach to Multiple Testing Under Dependence
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Factor profiled sure independence screening
- Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Forecasting Using Principal Components From a Large Number of Predictors
- Community Detection and Stochastic Block Models
- An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation
- Optimal Approximate Matrix Product in Terms of Stable Rank
- Near-Optimal Bounds for Phase Synchronization
- Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
- A Direct Approach to False Discovery Rates
- Embracing the Blessing of Dimensionality in Factor Models
- Viewing Direction Estimation in Cryo-EM Using Synchronization
- Cross-Dimensional Inference of Dependent High-Dimensional Data
- FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control
- On Consistency and Sparsity for Principal Components Analysis in High Dimensions
- Correlated z-Values and the Accuracy of Large-Scale Statistical Estimates
- A useful variant of the Davis–Kahan theorem for statisticians
- Correlation and Large-Scale Simultaneous Significance Testing
- Regularization and Variable Selection Via the Elastic Net
- Achieving Optimal Misclassification Proportion in Stochastic Block Model
- Estimation of the False Discovery Proportion with Unknown Dependence
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- Sketching for Principal Component Regression
- Common risk factors in the returns on stocks and bonds
- Determining the Number of Factors in Approximate Factor Models
- Robust Estimation of a Location Parameter
- The Rotation of Eigenvectors by a Perturbation. III
- Perturbation bounds in connection with singular value decomposition
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
