shrinkcovmat.unequal: Shrinking the Sample Covariance Matrix Towards a Diagonal...

In ShrinkCovMat: Shrinkage Covariance Matrix Estimators

Description

Provides a nonparametric Stein-type shrinkage estimator of the covariance matrix that is a linear combination of the sample covariance matrix and of the diagonal matrix with elements the corresponding sample variances on the diagonal and zeros elsewhere.

Usage

shrinkcovmat.unequal(data, centered = FALSE)

Arguments

data	a numeric matrix containing the data.
centered	a logical indicating if the vectors are centered around their mean vector.

Details

The rows of the data matrix data correspond to variables and the columns to subjects.

Value

Returns an object of the class 'shrinkcovmathat' that has components:

Sigmahat	The Stein-type shrinkage estimator of the covariance matrix.
lambdahat	The estimated optimal shrinkage intensity.
Sigmasample	The sample covariance matrix.
Target	The target covariance matrix.
centered	If the data are centered around their mean vector.

Author(s)

Anestis Touloumis

References

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

See Also

shrinkcovmat.equal and shrinkcovmat.identity.

Examples

data(colon)
normal_group <- colon[, 1:40]
tumor_group <- colon[, 41:62]
sigma_hat_normal_group <- shrinkcovmat.unequal(normal_group)
sigma_hat_normal_group
sigma_hat_tumor_group <- shrinkcovmat.unequal(tumor_group)
sigma_hat_tumor_group

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