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

Claudia Angelini, Daniela De Canditiis and Anna Plaksienko ()
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Claudia Angelini: Istituto per le Applicazioni del Calcolo “Mauro Picone”, CNR-Napoli, 80131 Naples, Italy
Daniela De Canditiis: Istituto per le Applicazioni del Calcolo “Mauro Picone”, CNR-Roma, 00185 Rome, Italy
Anna Plaksienko: Istituto per le Applicazioni del Calcolo “Mauro Picone”, CNR-Napoli, 80131 Naples, Italy

Mathematics, 2022, vol. 10, issue 21, 1-20

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 \({\texttt{jewel}}\).

Keywords: group lasso penalty; data integration; network estimation; stability selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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