A Comparative Study of Covariance Matrix Estimators in High-Dimensional Data
고차원 데이터에서 공분산행렬의 추정에 대한 비교연구

DongHyuk Lee, Jae Won Lee
2013 Korean Journal of Applied Statistics  
The covariance matrix is important in multivariate statistical analysis and a sample covariance matrix is used as an estimator of the covariance matrix. High dimensional data has a larger dimension than the sample size; therefore, the sample covariance matrix may not be suitable since it is known to perform poorly and even not invertible. A number of covariance matrix estimators have been recently proposed with three different approaches of shrinkage, thresholding, and modified Cholesky
more » ... ed Cholesky decomposition. We compare the performance of these newly proposed estimators in various situations.
doi:10.5351/kjas.2013.26.5.747 fatcat:2vqxldjngvd7vm4hwibjt5h6ui