Abstract
In this paper, we consider the problem of estimating a high dimensional precision matrix of Gaussian graphical model. Taking advantage of the connection between multivariate linear regression and entries of the precision matrix, we propose Bayesian Lasso together with neighborhood regression estimate for Gaussian graphical model. This method can obtain parameter estimation and model selection simultaneously. Moreover, the proposed method can provide symmetric confidence intervals of all entries of the precision matrix.
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We would like to thank all Associate Editors and gratefully acknowledge the helpful comments of the reviewers that substantially improved the paper.
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Supported by the National Natural Science Foundation of China (No. 11571080).
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Li, Fq., Zhang, Xs. Bayesian Lasso with neighborhood regression method for Gaussian graphical model. Acta Math. Appl. Sin. Engl. Ser. 33, 485–496 (2017). https://doi.org/10.1007/s10255-017-0676-z
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DOI: https://doi.org/10.1007/s10255-017-0676-z