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Zoom‐in–out joint graphical lasso for different coarseness scales
Author(s) -
Pircalabelu Eugen,
Claeskens Gerda,
Waldorp Lourens J.
Publication year - 2020
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12378
Subject(s) - zoom , scale (ratio) , lasso (programming language) , computer science , joint (building) , algorithm , artificial intelligence , pattern recognition (psychology) , data mining , geology , engineering , physics , quantum mechanics , architectural engineering , petroleum engineering , lens (geology) , world wide web
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.