nlShrinkLWEst: Analytical Non-Linear Shrinkage Estimator

In cvCovEst: Cross-Validated Covariance Matrix Estimation

Analytical Non-Linear Shrinkage Estimator

Description

nlShrinkLWEst() invokes the analytical estimator presented by \insertCiteLedoit2020;textualcvCovEst for applying a nonlinear shrinkage function to the sample eigenvalues of the covariance matrix. The shrinkage function relies on an application of the Hilbert Transform to an estimate of the sample eigenvalues' limiting spectral density. This estimated density is computed with the Epanechnikov kernel using a global bandwidth parameter of n^(-1/3). The resulting shrinkage function pulls eigenvalues towards the nearest mode of their empirical distribution, thus creating a localized shrinkage effect rather than a global one.

We do not recommend that this estimator be employed when the estimand is the correlation matrix. The diagonal entries of the resulting estimate are not guaranteed to be equal to one.

Usage

nlShrinkLWEst(dat)

Arguments

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

Value

A matrix corresponding to the estimate of the covariance matrix.

References

\insertAllCited

Examples

nlShrinkLWEst(dat = mtcars)

cvCovEst documentation built on May 29, 2024, 5:51 a.m.