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A joint convex penalty for inverse covariance matrix estimation.
- Source :
-
Computational Statistics & Data Analysis . Jul2014, Vol. 75, p15-27. 13p. - Publication Year :
- 2014
-
Abstract
- Abstract: The paper proposes a joint convex penalty for estimating the Gaussian inverse covariance matrix. A proximal gradient method is developed to solve the resulting optimization problem with more than one penalty constraints. The analysis shows that imposing a single constraint is not enough and the estimator can be improved by a trade-off between two convex penalties. The developed framework can be extended to solve wide arrays of constrained convex optimization problems. A simulation study is carried out to compare the performance of the proposed method to graphical lasso and the SPICE estimate of the inverse covariance matrix. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01679473
- Volume :
- 75
- Database :
- Academic Search Index
- Journal :
- Computational Statistics & Data Analysis
- Publication Type :
- Periodical
- Accession number :
- 95018425
- Full Text :
- https://doi.org/10.1016/j.csda.2014.01.015