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.
|Date of publication||2014-08-15 06:56:38|
|Maintainer||Binhuan Wang <email@example.com>|
AR1: Covariance Matrix with AR(1) Structure
banding: A Banding Operator on A Matrix
CVTuningCov-package: Select Tuning Parameters based on CV in Regularized...
hard.thresholding: Hard-thresholding Operator on A Covariance Matrix
random.CV: Select Tuning Parameter for Regularized Covariance Matrix by...
regular.CV: Select Tuning Parameter for Regularized Covariance Matrix by...
soft.thresholding: Soft-thresholding Operator on A Covariance Matrix
tapering: A Tapering Operator on A Matrix