banding.cv: Select Tuning Parameter for Banding Covariance Matrix by CV

In yanyachen/FinCovRegularization: Covariance Matrix Estimation and Regularization for Finance

Description

Apply K-fold cross-validation for selecting tuning parameters for banding covariance matrix using grid search strategy

Usage

banding.cv(matrix, n.cv = 10, norm = "F", seed = 142857)

Arguments

matrix	a N*p matrix, N indicates sample size and p indicates the dimension
n.cv	times that cross-validation repeated, the default number is 10
norm	the norms used to measure the cross-validation errors, which can be the Frobenius norm "F" or the operator norm "O"
seed	random seed, the default value is 142857

Details

For cross-validation, this function split the sample randomly into two pieces of size n1 = n-n/log(n) and n2 = n/log(n), and repeat this k times

Value

An object of class "CovCv" containing the cross-validation's result for covariance matrix regularization, including:

regularization	regularization method, which is "Banding"
parameter.opt	selected optimal parameter by cross-validation
cv.error	the corresponding cross-validation errors
n.cv	times that cross-validation repeated
norm	the norm used to measure the cross-validation error
seed	random seed

References

"High-Dimensional Covariance Estimation" by Mohsen Pourahmadi

Examples

data(m.excess.c10sp9003)
retcov.cv <- banding.cv(m.excess.c10sp9003, n.cv = 10,
                        norm = "F", seed = 142857)
summary(retcov.cv)
plot(retcov.cv)
# Low dimension

yanyachen/FinCovRegularization documentation built on May 4, 2019, 2:30 p.m.