On the behaviour of the smallest eigenvalue of a high-dimensional sample covariance matrix

2013 ◽  
Vol 68 (3) ◽  
pp. 569-570 ◽  
Author(s):  
P A Yaskov
Keyword(s):  
Covariance Matrix ◽  
Sample Covariance Matrix ◽  
High Dimensional ◽  
Smallest Eigenvalue ◽  
Sample Covariance

A Tapering Method in the Construction of Covariance Regularization

2012 ◽  
Vol 204-208 ◽  
pp. 4734-4737
Author(s):  
Shi Qing Wang ◽  
Fei Xia Song
Keyword(s):  
Statistical Analysis ◽  
Covariance Matrix ◽  
High Dimensional Data ◽  
Scientific Investigation ◽  
Sample Covariance Matrix ◽  
High Dimensional ◽  
Covariance Regularization ◽  
Sample Covariance ◽  
Wide Range

Estimation of Population covariance matrix from samples is important in a wide range of areas of statistical analysis. In the estimation, the sample covariance matrix, which is the most natural and standard estimator, often performs badly. With the collection of large high-dimensional data in scientific investigation, the related covariance matrix becomes complicated to deal with. Therefore, for the convenience in computing and analyzing, we need to simplify the covariance matrix. This method is referred as regularization. In this paper, we will consider a proof for a construction of covariance regularization by tapering.

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