Mixed scale joint graphical lasso

Eugen Pircalabelu, Gerda Claeskens and Lourens Waldorp

No 540397, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: We develop a method for estimating brain networks from fMRI datasets that have not all been measured using the same set of brain regions. Some of the coarse scale regions have been split in smaller subregions. The proposed penalized estimation procedure selects undirected graphical models with similar structures that combine information from several subjects and several coarseness scales. Both within scale edges and between scale edges that identify possible connections between a large region and its subregions are estimated.

Keywords: Mixed scale data; Fused and Group lasso; Joint graphical lasso; ℓ1 penalization; Sparsistency (search for similar items in EconPapers)
Date: 2016-05
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Published in FEB Research Report KBI_1616

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Persistent link: https://EconPapers.repec.org/RePEc:ete:kbiper:540397

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