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Mixed scale joint graphical lasso

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  • Pircalabelu, Eugen

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Claeskens, Gerda
  • Waldorp, Lourens J.

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.

Suggested Citation

  • Pircalabelu, Eugen & Claeskens, Gerda & Waldorp, Lourens J., 2016. "Mixed scale joint graphical lasso," LIDAM Reprints ISBA 2016049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2016049
    DOI: https://doi.org/10.1093/biostatistics/kxw025
    Note: In: Biostatistics, vol. 17(4), p. 793-806 (2016)
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    Cited by:

    1. Pircalabelu, Eugen & Claeskens, Gerda, 2021. "Linear manifold modeling and graph estimation based on multivariate functional data with different coarseness scales," LIDAM Discussion Papers ISBA 2021032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Pircalabelu, Eugen & Artemiou, Andreas, 2021. "Graph informed sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).

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