Description Usage Arguments Details Value References Examples
View source: R/cov-estim-lwlin.R
Computes the Ledoit-Wolf linear shrinkage estimator of the covariance matrix towards the identity matrix.
1 | sigma_estim_lwident(data, shrink_int = NULL, zeromean_log = FALSE)
|
data |
an nxp data matrix. |
shrink_int |
a double, indicating the shrinkage intensity. Default is the optimal shrinkage intensity as in \insertCiteledoit2003identity;textualCovEstim. |
zeromean_log |
a logical, indicating whether the data matrix has zero means (TRUE) or not (FALSE). Default value is FALSE. |
The Ledoit-Wolf linear shrinkage estimator of the covariance matrix towards the identity matrix is calculated with the following formula:
\hat{Σ}= sΣ_{T} + (1-s)Σ,
where Σ is the sample covariance matrix, s is the user-supplied or optimal shrinkage intensity and Σ_{T} is a pxp identity matrix. This covariance estimator assumes a zero correlation and variances of one as the underlying covariance structure of the data. A corresponding MATLAB code for the estimator can be accessed under https://www.econ.uzh.ch/en/people/faculty/wolf/publications.html.
a list with the following entries
a pxp estimated covariance matrix.
an estimation specific tuning parameter, here the shrinkage intensity.
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