CVTuningCov: Regularized Estimators of Covariance Matrices with CV Tuning

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.

Author
Binhuan Wang
Date of publication
2014-08-15 06:56:38
Maintainer
Binhuan Wang <binhuan.wang@nyumc.org>
License
GPL-2
Version
1.0

View on CRAN

Man pages

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

Files in this package

CVTuningCov
CVTuningCov/NAMESPACE
CVTuningCov/R
CVTuningCov/R/functions.R
CVTuningCov/MD5
CVTuningCov/DESCRIPTION
CVTuningCov/man
CVTuningCov/man/hard.thresholding.Rd
CVTuningCov/man/banding.Rd
CVTuningCov/man/F.norm2.rd
CVTuningCov/man/L2.norm2.rd
CVTuningCov/man/CVTuningCov-package.Rd
CVTuningCov/man/regular.CV.Rd
CVTuningCov/man/random.CV.Rd
CVTuningCov/man/soft.thresholding.Rd
CVTuningCov/man/tapering.Rd
CVTuningCov/man/AR1.Rd