Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators

Romain Couillet; Matthew R. McKay

doi:10.1016/j.jmva.2014.06.018

ScienceGate Book Chapters

JOURNAL ARTICLE

Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators

Romain Couillet Matthew R. McKay

Year: 2014 Journal: Journal of Multivariate Analysis Vol: 131 Pages: 99-120 Publisher: Elsevier BV

DOI: 10.1016/j.jmva.2014.06.018

Get Full-Text PDF Get Analytical Report

Keywords:

Estimator Mathematics Scatter matrix Outlier Covariance matrix Shrinkage estimator Shrinkage Applied mathematics Population Estimation of covariance matrices Robustness (evolution) Statistics Covariance Minimax estimator Minimum-variance unbiased estimator

Metrics

107

Cited By

14.87

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Random Matrices and Applications

Physical Sciences → Mathematics → Statistics and Probability

Advanced Statistical Methods and Models

Physical Sciences → Mathematics → Statistics and Probability

Point processes and geometric inequalities

Physical Sciences → Mathematics → Applied Mathematics

Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators

Metrics

Citation History

Topics

Related Documents

Large Dimensional Analysis of Robust M-Estimators of Covariance With Outliers

Nonparametric Stein-type shrinkage covariance matrix estimators in high-dimensional settings

Random matrix analysis of Gram matrices and large robust covariance matrix estimators with applications

Nonlinear shrinkage test on a large‐dimensional covariance matrix

Linear Shrinkage-Based Hypothesis Test for Large-Dimensional Covariance Matrix