Cross-Validated Covariance Matrix Estimation
Adaptive LASSO Estimator
Adaptive LASSO Thresholding Function
Banding Estimator
Showing Best Estimator Within Each Class of Estimators
Check Arguments Passed to cvCovEst
Check Arguments Passed to plot.cvCovEst and summary.cvCovEst
Estimate C of Spiked Covariance Matrix Estimator
Estimate Ell of Spiked Covariance Matrix Estimator
Estimate S of Spiked Covariance Matrix Estimator
Cross-Validated Covariance Matrix Estimator Selector
Eigenvalue Plot
Cross-Validation Function for Aggregated Frobenius Loss
Cross-Validation Function for Matrix Frobenius Loss
Matrix Metrics for cvCovEst Object
Multiple Heat Map Plot
Summary Statistics of Cross-Validated Risk by Estimator Class
Cross-Validated Risk Plot
Cross-Validation Function for Scaled Matrix Frobenius Loss
Summary Plot
Linear Shrinkage Estimator, Dense Target
Estimator Attributes Function
Estimate Noise in Spiked Covariance Matrix Model
Hyperparameter Retrieval Function
Summarize Cross-Validated Risks by Class with Hyperparameter
Check for cvCovEst Class
Linear Shrinkage Estimator
Ledoit-Wolf Linear Shrinkage Estimator
General Matrix Metrics
Multi-Hyperparameter Risk Plots
Analytical Non-Linear Shrinkage Estimator
Generic Plot Method for cvCovEst
Plot adaptiveLassoEst
Plot poetEst
Plot robustPoetEst
POET Estimator
Robust POET Estimator for Elliptical Distributions
Safe Centering and Scaling of Columns
Sample Covariance Matrix
Smoothly Clipped Absolute Deviation Estimator
Smoothly Clipped Absolute Deviation Thresholding Function
Extract Estimated Scaled Eigenvalues in Spiked Covariance Matrix Model
Frobenius Norm Shrinkage Estimator, Spiked Covariance Model
Operator Norm Shrinkage Estimator, Spiked Covariance Model
Stein Loss Shrinkage Estimator, Spiked Covariance Model
Convert String to Numeric or Integer When Needed
Generic Summary Method for cvCovEst
Tapering Estimator
cvCovEst Plot Theme
Hard Thresholding Estimator
An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.