sigma_estim_pca: PCA Covariance Estimation

In antshi/CovEstim: (High-dimensional) Covariance Estimation

Description

Computes a PCA estimator of the covariance matrix.

Usage

sigma_estim_pca(data, number_pc = NULL)

Arguments

data	an nxp data matrix
number_pc	an integer, indicating the number of principal components. Default value is NULL and the number of principal components is set according to the Marcenko-Pastur edge as in \insertCitemarvcenko1967distribution;textualCovEstim.

Value

a list with the following entries

• a pxp estimated covariance matrix.

• an estimation specific tuning parameter, here the number of principal components.

References

\insertRef

johnson2002appliedCovEstim

\insertRef

fan2016overviewCovEstim

Examples

data(sp200)
sp_rets <- sp200[1:100,-1]
sigma_pca2 <- sigma_estim_pca(sp_rets, number_pc=2)[[1]] # user-defined number of factors
results_pca_mp <- sigma_estim_pca(sp_rets) # number of factors, defined with MP cut-edge
sigma_pca_mp <- results_pca_mp[[1]]
number_pc <- results_pca_mp[[2]]

antshi/CovEstim documentation built on Nov. 13, 2020, 2:25 p.m.