Positive definite estimation of large covariance matrix using generalized nonconvex penalties

Positive definite estimation of large covariance matrix using generalized nonconvex penalties

This paper addresses the issue of large covariance matrix estimation in a high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity...

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Bibliographic Details
Main Authors: Fei Wen, Yuan Yang, Peilin Liu, Robert C. Qiu
Format: Article
Language:English
Published: IEEE 2016-01-01
Series:IEEE Access
Subjects:
Covariance matrix estimation
covariance sketching
alternating direction method
positive-definite estimation
nonconvex optimization
sparse
Online Access:https://ieeexplore.ieee.org/document/7526330/

Internet

https://ieeexplore.ieee.org/document/7526330/

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