Matrix means and a novel high-dimensional shrinkage phenomenon
From MaRDI portal
Publication:2676932
DOI10.3150/21-BEJ1430WikidataQ114038739 ScholiaQ114038739MaRDI QIDQ2676932
Keith Levin, Elizaveta Levina, Asad Lodhia
Publication date: 28 September 2022
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1910.07434
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A well-conditioned estimator for large-dimensional covariance matrices
- Nonlinear shrinkage estimation of large-dimensional covariance matrices
- Nonparametric eigenvalue-regularized precision or covariance matrix estimator
- Optimal detection of sparse principal components in high dimension
- Estimation of the population spectral distribution from a large dimensional sample covariance matrix
- Free probability and random matrices
- The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
- Optimal rates of convergence for sparse covariance matrix estimation
- Principal components in linear mixed models with general bulk
- Improved Stein-type shrinkage estimators for the high-dimensional multivariate normal covariance matrix
- Optimal rates of convergence for covariance matrix estimation
- Covariance regularization by thresholding
- Spectrum estimation for large dimensional covariance matrices using random matrix theory
- Finite sample approximation results for principal component analysis: A matrix perturbation approach
- Statistical eigen-inference from large Wishart matrices
- Spectral analysis of large dimensional random matrices
- Estimation of a covariance matrix under Stein's loss
- Lectures on the theory of estimation of many parameters
- Riemannian geometry of Grassmann manifolds with a view on algorithmic computation
- Nonparametric Stein-type shrinkage covariance matrix estimators in high-dimensional settings
- Spectrum estimation from samples
- Optimal estimation of a large-dimensional covariance matrix under Stein's loss
- The norm of polynomials in large random and deterministic matrices
- Averages of unlabeled networks: geometric characterization and asymptotic behavior
- Matrix estimation by universal singular value thresholding
- Optimal estimation and rank detection for sparse spiked covariance matrices
- Optimal shrinkage of eigenvalues in the spiked covariance model
- Estimation of covariance and precision matrices under scale-invariant quadratic loss in high dimension
- Regularized estimation of large covariance matrices
- Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
- Nonparametric estimation of large covariance matrices of longitudinal data
- Shrinkage Estimators for Covariance Matrices
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Harmonic means of Wishart random matrices
- The Geometry of Algorithms with Orthogonality Constraints
- Shrinkage Algorithms for MMSE Covariance Estimation
- An overview of the estimation of large covariance and precision matrices
- Large Sample Covariance Matrices and High-Dimensional Data Analysis
- On Consistency and Sparsity for Principal Components Analysis in High Dimensions
- Generalized Thresholding of Large Covariance Matrices
- A useful variant of the Davis–Kahan theorem for statisticians
- Covariance, subspace, and intrinsic Crame/spl acute/r-Rao bounds
- The Rotation of Eigenvectors by a Perturbation. III
- Linear Statistical Inference and its Applications
- Condition-Number-Regularized Covariance Estimation
- Modeling Network Populations via Graph Distances
- Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation
- Lognormal Distributions and Geometric Averages of Symmetric Positive Definite Matrices
