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Bayesian Lasso with neighborhood regression method for Gaussian graphical model

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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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Acknowledgements

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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Authors and Affiliations

  1. Department of Statistics, School of Management, Fudan University, Shanghai, 200433, China

    Fan-qun Li & Xin-sheng Zhang

  2. Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233000, China

    Fan-qun Li

Authors
  1. Fan-qun Li
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  2. Xin-sheng Zhang
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Corresponding author

Correspondence to Fan-qun Li.

Additional information

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

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