Year: 2021
Author: Didi Lv, Xiaoqun Zhang
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 5 : pp. 693–707
Abstract
Precision matrix estimation is an important problem in statistical data analysis. This paper proposes a sparse precision matrix estimation approach, based on CLIME estimator and an efficient algorithm GISS$^{{\rho}}$ that was originally proposed for $l_1$ sparse signal recovery in compressed sensing. The asymptotic convergence rate for sparse precision matrix estimation is analyzed with respect to the new stopping criteria of the proposed GISS$^{{\rho}}$ algorithm. Finally, numerical comparison of GISS$^{\rho}$ with other sparse recovery algorithms, such as ADMM and HTP in three settings of precision matrix estimation is provided and the numerical results show the advantages of the proposed algorithm.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.2005-m2019-0151
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 5 : pp. 693–707
Published online: 2021-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 15
Keywords: Precision matrix estimation CLIME estimator Sparse recovery Inverse scale space method Greedy methods.