Add to ORKGProvides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.<br/
AbstractFor high dimensional data sets the sample covariance matrix is usually unbiased but noisy if...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
This paper establishes the first analytical formula for optimal nonlinear shrinkage of large-dimensi...
Estimating a covariance matrix is an important task in applications where the number of vari-ables i...
This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal d...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
International audienceIn the context of robust covariance matrix estimation, this work generalizes t...
International audienceIn the context of robust covariance matrix estimation, this work generalizes t...
Covariance estimation is a key step in many target detection algorithms. To distinguish target from ...
For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sam...
When estimating covariance matrices, traditional sample covariance-based estimators are straightforw...
The paper proposes a cross-validated linear shrinkage estimation for population covariance matrices....
Description This package implements a James-Stein-type shrinkage estimator for the covariance matrix...
AbstractFor high dimensional data sets the sample covariance matrix is usually unbiased but noisy if...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
This paper establishes the first analytical formula for optimal nonlinear shrinkage of large-dimensi...
Estimating a covariance matrix is an important task in applications where the number of vari-ables i...
This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal d...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
International audienceIn the context of robust covariance matrix estimation, this work generalizes t...
International audienceIn the context of robust covariance matrix estimation, this work generalizes t...
Covariance estimation is a key step in many target detection algorithms. To distinguish target from ...
For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sam...
When estimating covariance matrices, traditional sample covariance-based estimators are straightforw...
The paper proposes a cross-validated linear shrinkage estimation for population covariance matrices....
Description This package implements a James-Stein-type shrinkage estimator for the covariance matrix...
AbstractFor high dimensional data sets the sample covariance matrix is usually unbiased but noisy if...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
This paper establishes the first analytical formula for optimal nonlinear shrinkage of large-dimensi...