ShrinkCovMat (version 1.0.2)

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

Description

This function 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 diagonal elements the corresponding sample variances.

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.

Value

• Returns an object of the class "covmat" that has components:
• Sigmahat
  The Stein-type shrinkage estimator of the covariance matrix.
• lambdahat
  The estimated optimal shrinkage intensity.
• Sigmasam
  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. (2014). Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings. To appear in Computational Statistics and Data Analysis, http://arxiv.org/abs/1410.4726.

See Also

shrinkcovmat.equal and shrinkcovmat.identity.

Examples

data(colon)
normal.group <- colon[,1:40]
colon.group <- colon[,41:62]
Sigma.normal <- shrinkcovmat.unequal(normal.group)
Sigma.normal
Sigma.colon <- shrinkcovmat.unequal(colon.group)
Sigma.colon