Trimmed estimators for large dimensional sparse covariance matrices
2018 ◽
Vol 08
(01)
◽
pp. 1950003
Keyword(s):
In this paper, we will propose two new estimators for sparse covariance matrix. Our starting point is to make the estimator of each element of covariance matrix more robust. More precisely, we will trim the observations for each pairwise product of components of population as a first step. Then we form the sample covariance matrices based on the trimmed data. Finally, we apply the thresholding to the derived sample covariance matrices. These two new estimators will be shown to achieve the optimal convergence rate.
1993 ◽
Vol 21
(2)
◽
pp. 649-672
◽
2020 ◽
Vol 10
(01)
◽
pp. 2150014
2015 ◽
Vol 164
(1-2)
◽
pp. 459-552
◽
Keyword(s):