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- W2051234290 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." @default.
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- W2051234290 date "2014-07-01" @default.
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- W2051234290 title "A joint convex penalty for inverse covariance matrix estimation" @default.
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- W2051234290 doi "https://doi.org/10.1016/j.csda.2014.01.015" @default.
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