linearShrinkLWEst {cvCovEst} R Documentation

## Ledoit-Wolf Linear Shrinkage Estimator

### Description

linearShrinkLWEst() computes an asymptotically optimal convex combination of the sample covariance matrix and the identity matrix. This convex combination effectively shrinks the eigenvalues of the sample covariance matrix towards the identity. This estimator is more accurate than the sample covariance matrix in high-dimensional settings under fairly loose assumptions. For more information, consider reviewing the manuscript by Ledoit and Wolf (2004).

### Usage

linearShrinkLWEst(dat)


### Arguments

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

### Value

A matrix corresponding to the Ledoit-Wolf linear shrinkage estimate of the covariance matrix.

### References

Ledoit O, Wolf M (2004). “A well-conditioned estimator for large-dimensional covariance matrices.” Journal of Multivariate Analysis, 88(2), 365 - 411. ISSN 0047-259X, doi: 10.1016/S0047-259X(03)00096-4, https://www.sciencedirect.com/science/article/pii/S0047259X03000964.

### Examples

linearShrinkLWEst(dat = mtcars)


[Package cvCovEst version 1.1.0 Index]