Optimal estimation of a large-dimensional covariance matrix under Stein's loss
type: Item
instance of: scholarly article
title: Optimal estimation of a large-dimensional covariance matrix under Stein's loss
zbMATH Open document ID: 1415.62032
DOI: 10.3150/17-BEJ979
author: Olivier Ledoit
author: Michael Wolf
published in: Bernoulli
publication date: 2018-05-18T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.bj/1524038770
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 60B20
Mathematics Subject Classification ID: 62J07
zbMATH DE Number: 6869892
zbMATH Keywords: large-dimensional asymptotics
zbMATH Keywords: nonlinear shrinkage estimation
zbMATH Keywords: random matrix theory
zbMATH Keywords: rotation equivariance
zbMATH Keywords: Stein's loss
describes a project that uses: SNOPT
MaRDI profile type: Publication
cites work: Spectral analysis of large dimensional random matrices
cites work: No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices
cites work: Exact separation of eigenvalues of large dimensional sample covariance matrices
cites work: CLT for linear spectral statistics of large-dimensional sample covariance matrices.
cites work: A note on the largest eigenvalue of a large dimensional sample covariance matrix
cites work: Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
cites work: Eigenvalues of large sample covariance matrices of spiked population models
cites work: Covariance regularization by thresholding
cites work: Robust Shrinkage Estimation of High-Dimensional Covariance Matrices
cites work: Shrinkage Estimators for Covariance Matrices
cites work: Estimation of a covariance matrix under Stein's loss
cites work: Optimal shrinkage of eigenvalues in the spiked covariance model
cites work: Spectrum estimation for large dimensional covariance matrices using random matrix theory
cites work: SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
cites work: Empirical Bayes estimation of the multivariate normal covariance matrix
cites work: Q4039888
cites work: Q3185327
cites work: An invariant form for the prior probability in estimation problems
cites work: On the distribution of the largest eigenvalue in principal components analysis
cites work: A Convex Pseudolikelihood Framework for High Dimensional Partial Correlation Estimation with Convergence Guarantees
cites work: On Information and Sufficiency
cites work: Eigenvectors of some large sample covariance matrix ensembles
cites work: A well-conditioned estimator for large-dimensional covariance matrices
cites work: Nonlinear shrinkage estimation of large-dimensional covariance matrices
cites work: Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions
cites work: Q3723503
cites work: DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
cites work: Improved Estimation of Eigenvalues and Eigenvectors of Covariance Matrices Using Their Sample Estimates
cites work: Q3219581
cites work: A theoretical study of Stein's covariance estimator
cites work: Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
cites work: On the empirical distribution of eigenvalues of a class of large dimensional random matrices
cites work: Analysis of the limiting spectral distribution of large dimensional random matrices
cites work: Lectures on the theory of estimation of many parameters
cites work: Estimating the Inverse Matrix of Scale Parameters in an Elliptically Contoured Distribution
cites work: Condition-Number-Regularized Covariance Estimation
cites work: On the limit of the largest eigenvalue of the large dimensional sample covariance matrix
OpenAlex ID: W3123766383
description: scientific article; zbMATH DE number 6869892