Covariance Matrix Estimation (High Dimensional)

Abstract

The article concerns estimation of high‐dimensional covariance matrices. In this case, the sample covariance matrix estimate is typically not consistent. For an improvement, we present various regularization procedures including shrinkage, penalized likelihood estimation, Cholesky decomposition, sparse principal component analysis, and elementwise shrinkage by banding, tapering, and thresholding.