ShrinkCovMat (version 1.1.2)

shrinkcovmat.identity: Shrinking the Sample Covariance Matrix Towards the Identity Matrix

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 identity matrix.

Usage

shrinkcovmat.identity(data, centered = FALSE)

Arguments

data

a numeric matrix containing the data.

centered

a logical indicating if the mean vector is the zero vector.

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.

Details

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

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.unequal.

Examples

# NOT RUN {
data(colon)
normal.group <- colon[, 1:40]
colon.group <- colon[, 41:62]
Sigmahat.normal <- shrinkcovmat.identity(normal.group)
Sigmahat.normal
Sigmahat.colon <- shrinkcovmat.identity(colon.group)
Sigmahat.colon
# }