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Title: Sparse graphical modelling via the sorted l1 - norm Authors:  Riccardo Riccobello - University of Trento (Italy)
Malgorzata Bogdan - University of Wroclaw (Poland)
Giovanni Bonaccolto - University of Enna Kore (Italy)
Philipp Johannes Kremer - EBS Universitaet fuer Wirtschaft und Recht (Germany)
Sandra Paterlini - University of Trento (Italy) [presenting]
Abstract: Sparse graphical modelling has attained widespread attention across various academic fields. We propose two new graphical model approaches, Gslope and Tslope, which provide sparse estimates of the precision matrix by penalizing its sorted L1-norm, and relying on Gaussian and t-Student data, respectively. In extensive simulation and real-world analysis, the new methods are compared to other state-of-the-art graphical modelling approaches. The results establish GSlope and TSlope as two new effective tools for sparse network estimation.