Positive-Definite โ<sub>1</sub>-Penalized Estimation of Large Covariance Matrices
Positive-Definite โ<sub>1</sub>-Penalized Estimation of Large Covariance Matrices
The thresholding covariance estimator has nice asymptotic properties for estimating sparse large covariance matrices, but it often has negative eigenvalues when used in real data analysis. To fix this drawback of thresholding estimation, we develop a positive-definite โ1-penalized covariance estimator for estimating sparse large covariance matrices. We derive an efficient โฆ