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• W3124450788 abstract "In this paper, we focus on the estimation of a high-dimensional precision matrix. We propose a simple improvement of the graphical lasso framework (glasso) that is able to attain better statistical performance without sacrificing too much the computational cost. The proposed improvement is based on computing a root of the covariance matrix to reduce the spread of the associated eigenvalues, and maintains the original convergence rate. Through extensive numerical results, using both simulated and real datasets, we show the proposed modification outperforms the glasso procedure. Finally, our results show that the square-root improvement may be a reasonable choice in practice" @default.
• W3124450788 created "2021-02-01" @default.
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• W3124450788 date "2014-05-01" @default.
• W3124450788 modified "2023-09-27" @default.
• W3124450788 title "Improving the graphical lasso estimation for the precision matrix through roots ot the sample convariance matrix" @default.
• W3124450788 hasPublicationYear "2014" @default.
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