1 research outputs found
Sparse Graphical Modelling via the Sorted L-Norm
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.
We provide the selections of the tuning parameters which provably control the
probability of including false edges between the disjoint graph components and
empirically control the False Discovery Rate for the block diagonal covariance
matrices. In extensive simulation and real world analysis, the new methods are
compared to other state-of-the-art sparse graphical modelling approaches. The
results establish Gslope and Tslope as two new effective tools for sparse
network estimation, when dealing with both Gaussian, t-student and mixture
data