Zoom-In–Out Joint Graphical Lasso for Different Coarseness Scales. Issue 1 (21st September 2019)

• Record Type: Journal Article
• Authors: Pircalabelu, Eugen; Claeskens, Gerda; Waldorp, Lourens J.
• Is Part Of: Journal of the Royal Statistical Society. Volume 69:Issue 1(2020)
• Page Start: 47
• Page End: 67
• Publication Date: 2019-09-21
• Subjects: Functional magnetic resonance imaging data -- Graphical lasso -- l1-penalization -- Mixed scale data -- Temporal dependence; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12378
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: A new method is proposed to estimate graphical models simultaneously 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 level of sparsity 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 an application on functional magnetic resonance brain imaging data show the method's performance in practice.