ShrinkCovMat-package: Shrinkage Covariance Matrix Estimators

Description Details Author(s) References See Also


Provides nonparametric Stein-type shrinkage estimators of the covariance matrix that are suitable and statistically efficient when the number of variables is larger than the sample size. These estimators are non-singular and well-conditioned regardless of the dimensionality.


Each of the implemented shrinkage covariance matrix estimators is a convex linear combination of the sample covariance matrix and of a target matrix. Three options are considered for the target matrix: (a) the diagonal matrix with diagonal elements the average of the sample variances (shrinkcovmat.equal), (b) the diagonal matrix with diagonal elements the corresponding sample variances (shrinkcovmat.unequal), and (c) the identity matrix (shrinkcovmat.identity). The optimal shrinkage intensity determines how much the sample covariance matrix will be shrunk towards the selected target matrix. Estimation of the corresponding optimal shrinkage intensities is discussed in Touloumis (2015). The function targetselection is designed to ease the selection of the target matrix.


Anestis Touloumis

Maintainer: Anestis Touloumis <>


Touloumis, A. (2015) Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings. Computational Statistics & Data Analysis 83, 251–261.

See Also

Useful links:

ShrinkCovMat documentation built on July 30, 2019, 9:03 a.m.