This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.
Maintainer: Binhuan Wang <[email protected]>
Fang, Y., Wang, B. and Feng, Y. (2013). Tuning parameter selection in regularized estimations of large covariance matrices. Available at: http://arxiv.org/abs/1308.3416.
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