Zoom-in/out joint graphical lasso for different coarseness scales

Pircalabelu, Eugen [UCL] Gerda Claeskens [KU Leuven]

A new method is proposed to simultaneously estimate graphical models from data obtained at different coarseness scales. Starting from a predefined scale the method offers the possibility to zoom in or out over scales on particular edges. The estimated graphs over the different scales have similar structures although their sparsity level depends on the scale at which estimation takes place. The method makes it possible to evaluate the evolution of the graphs from the coarsest to the finest scale or vice-versa. We select an optimal coarseness scale to be used for further analysis. Simulation studies and the application on fMRI brain imaging data show the method's performance in practice.