linshrink_cov: Linear-shrinkage estimator of population covariance matrix.

In nlshrink: Non-Linear Shrinkage Estimation of Population Eigenvalues and Covariance Matrices

Description

The linear shrinkage estimator of the population covariance matrix is computed by shrinking the sample covariance matrix towards the identity matrix based on a shrinkage factor. Note that the eigenvalues of the population covariance matrix estimate are not the same as the linear shrinkage estimates of population eigenvalues. Details in referenced publication.

Usage

linshrink_cov(X, k = 0)

Arguments

X	A data matrix.
k	(Optional) Non-negative integer less than ncol(X). If k == 0 (default), X is assumed to contain 1 class, which will be centered. If k >= 1, X is assumed to contain k classes, each of which has already been centered.

Value

Population covariance matrix estimate. A square positive semi-definite matrix of dimension ncol(X).

References

• Ledoit, O. and Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88(2)

Examples

linshrink_cov(X = matrix(rnorm(1e4, mean = 5), nrow = 100, ncol = 100)) # 1 class; will be centered
linshrink_cov(X = matrix(rnorm(1e4), nrow = 100, ncol = 100), k = 1) # 1 class; no centering

nlshrink documentation built on May 1, 2019, 8:42 p.m.