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

 `1` ```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

 ```1 2 3 4 5 6``` ```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 ```

FinCovRegularization documentation built on May 29, 2017, 11:47 a.m.