Jewel: A Novel Method for Joint Estimation of 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, 2021, vol. 9, issue 17, 1-24

Abstract: In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel , a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix’s symmetry and the graphs’ joint learning. We solve the minimization problem using the group descend algorithm and propose two procedures for estimating the regularization parameter. Furthermore, we establish the estimator’s consistency property. Finally, we illustrate our estimator’s performance through simulated and real data examples on gene regulatory networks.

Keywords: Gaussian Graphical Model; group Lasso; joint estimation; network estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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