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Jewel 2.0 : An Improved Joint Estimation Method for Multiple Gaussian Graphical Models

Author

Listed:
  • Claudia Angelini

    (Istituto per le Applicazioni del Calcolo “Mauro Picone”, CNR-Napoli, 80131 Naples, Italy
    These authors contributed equally to this work.)

  • Daniela De Canditiis

    (Istituto per le Applicazioni del Calcolo “Mauro Picone”, CNR-Roma, 00185 Rome, Italy
    These authors contributed equally to this work.)

  • Anna Plaksienko

    (Istituto per le Applicazioni del Calcolo “Mauro Picone”, CNR-Napoli, 80131 Naples, Italy
    These authors contributed equally to this work.)

Abstract

In this paper, we consider the problem of estimating the graphs of conditional dependencies between variables (i.e., graphical models) from multiple datasets under Gaussian settings. We present jewel 2.0 , which improves our previous method jewel 1.0 by modeling commonality and class-specific differences in the graph structures and better estimating graphs with hubs, making this new approach more appealing for biological data applications. We introduce these two improvements by modifying the regression-based problem formulation and the corresponding minimization algorithm. We also present, for the first time in the multiple graphs setting, a stability selection procedure to reduce the number of false positives in the estimated graphs. Finally, we illustrate the performance of jewel 2.0 through simulated and real data examples. The method is implemented in the new version of the R package \({\texttt{jewel}}\).

Suggested Citation

  • Claudia Angelini & Daniela De Canditiis & Anna Plaksienko, 2022. "Jewel 2.0 : An Improved Joint Estimation Method for Multiple Gaussian Graphical Models," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3983-:d:954357
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    References listed on IDEAS

    as
    1. Jian Guo & Elizaveta Levina & George Michailidis & Ji Zhu, 2011. "Joint estimation of multiple graphical models," Biometrika, Biometrika Trust, vol. 98(1), pages 1-15.
    2. Antonella Iuliano & Annalisa Occhipinti & Claudia Angelini & Italia De Feis & Pietro Liò, 2021. "COSMONET: An R Package for Survival Analysis Using Screening-Network Methods," Mathematics, MDPI, vol. 9(24), pages 1-25, December.
    3. Claudia Angelini & Daniela De Canditiis & Anna Plaksienko, 2021. "Jewel : A Novel Method for Joint Estimation of Gaussian Graphical Models," Mathematics, MDPI, vol. 9(17), pages 1-24, August.
    4. Patrick Danaher & Pei Wang & Daniela M. Witten, 2014. "The joint graphical lasso for inverse covariance estimation across multiple classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 373-397, March.
    Full references (including those not matched with items on IDEAS)

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