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