denseLinearShrinkEst {cvCovEst}

Linear Shrinkage Estimator, Dense Target

Description

denseLinearShrinkEst() computes the asymptotically optimal convex combination of the sample covariance matrix and a dense target matrix. This target matrix's diagonal elements are equal to the average of the sample covariance matrix estimate's diagonal elements, and its off-diagonal elements are equal to the average of the sample covariance matrix estimate's off-diagonal elements. For information on this estimator's derivation, see Ledoit and Wolf (2020) and Schäfer and Strimmer (2005).

Usage

denseLinearShrinkEst(dat)

Arguments

dat

A numeric data.frame, matrix, or similar object.

Value

A matrix corresponding to the estimate of the covariance matrix.

References

Ledoit O, Wolf M (2020). “The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation.” Journal of Financial Econometrics. ISSN 1479-8409, doi:10.1093/jjfinec/nbaa007, nbaa007, https://academic.oup.com/jfec/advance-article-pdf/doi/10.1093/jjfinec/nbaa007/33416890/nbaa007.pdf.

Schäfer J, Strimmer K (2005). “A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics.” Statistical Applications in Genetics and Molecular Biology, 4(1). doi:10.2202/1544-6115.1175, https://www.degruyter.com/view/journals/sagmb/4/1/article-sagmb.2005.4.1.1175.xml.xml.

Examples

denseLinearShrinkEst(dat = mtcars)