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 <A.Touloumis@brighton.ac.uk>

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

Useful links:

Report bugs at http://github.com/AnestisTouloumis/ShrinkCovMat/issues

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