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Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection

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  • Guido Consonni
  • Luca La Rocca
  • Stefano Peluso

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  • Guido Consonni & Luca La Rocca & Stefano Peluso, 2017. "Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 741-764, September.
    • Handle: RePEc:bla:scjsta:v:44:y:2017:i:3:p:741-764
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    File URL: http://hdl.handle.net/10.1111/sjos.12273
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    References listed on IDEAS

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    1. C. M. Carvalho & J. G. Scott, 2009. "Objective Bayesian model selection in Gaussian graphical models," Biometrika, Biometrika Trust, vol. 96(3), pages 497-512.
    2. Alberto Roverato, 2002. "Hyper Inverse Wishart Distribution for Non‐decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 391-411, September.
    3. Anindya Bhadra & Bani K. Mallick, 2013. "Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis," Biometrics, The International Biometric Society, vol. 69(2), pages 447-457, June.
    4. Mengjie Chen & Zhao Ren & Hongyu Zhao & Harrison Zhou, 2016. "Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 394-406, March.
    5. NESTEROV, Yu., 2005. "Smooth minimization of non-smooth functions," LIDAM Reprints CORE 1819, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Peter J. Green & Alun Thomas, 2013. "Sampling decomposable graphs using a Markov chain on junction trees," Biometrika, Biometrika Trust, vol. 100(1), pages 91-110.
    7. T. Tony Cai & Hongzhe Li & Weidong Liu & Jichun Xie, 2013. "Covariate-adjusted precision matrix estimation with an application in genetical genomics," Biometrika, Biometrika Trust, vol. 100(1), pages 139-156.
    8. Guido Consonni & Luca La Rocca, 2012. "Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(4), pages 743-756, December.
    9. Mathias Drton, 2004. "Model selection for Gaussian concentration graphs," Biometrika, Biometrika Trust, vol. 91(3), pages 591-602, September.
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    Cited by:

    1. Castelletti, Federico & Peluso, Stefano, 2021. "Equivalence class selection of categorical graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    2. Dan J. Spitzner, 2023. "Calibrated Bayes factors under flexible priors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 733-767, September.
    3. Federico Castelletti & Guido Consonni & Luca Rocca, 2022. "Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 261-267, June.
    4. Federico Castelletti, 2020. "Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures," International Statistical Review, International Statistical Institute, vol. 88(3), pages 752-775, December.
    5. Cao Xuan & Ding Lili & Mersha Tesfaye B., 2020. "Joint variable selection and network modeling for detecting eQTLs," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(1), pages 1-15, February.
    6. Zhang, Hongmei & Huang, Xianzheng & Han, Shengtong & Rezwan, Faisal I. & Karmaus, Wilfried & Arshad, Hasan & Holloway, John W., 2021. "Gaussian Bayesian network comparisons with graph ordering unknown," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    7. Nikolaos Petrakis & Stefano Peluso & Dimitris Fouskakis & Guido Consonni, 2020. "Objective methods for graphical structural learning," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 420-438, August.
    8. Laurenţiu Cătălin Hinoveanu & Fabrizio Leisen & Cristiano Villa, 2020. "A loss‐based prior for Gaussian graphical models," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 444-466, December.
    9. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

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