CVTuningCov-package: Select Tuning Parameters based on CV in Regularized...

In CVTuningCov: Regularized Estimators of Covariance Matrices with CV Tuning

Description

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.

Details

Package:	CVTuningCov
Type:	Package
Version:	1.0
Date:	2014-07-31
License:	GPL-2

Author(s)

Binhuan Wang

Maintainer: Binhuan Wang <[e-mail]>

References

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.

Examples

library(MASS);
n <- 50;
p <- 50;
fold <- 3;
k.grid <- seq(0,2*(p-1),by=1);
Sigma <- AR1(p, rho=0.6);
X <- mvrnorm(n,rep(0,p),Sigma);
CV.F.fit <- regular.CV(X,k.grid, method='Tapering',fold=fold,norm='F');
CV.F.fit$CV.k;

Example output

[1] 5 3

CVTuningCov documentation built on May 2, 2019, 7:23 a.m.