A joint convex penalty for inverse covariance matrix estimation

Ashwini Maurya

Computational Statistics & Data Analysis, 2014, vol. 75, issue C, 15-27

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.

Keywords: Proximal gradient; Joint penalty; Convex optimization; Sparsity (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:75:y:2014:i:c:p:15-27

DOI: 10.1016/j.csda.2014.01.015

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