scholarly journals Robust Gaussian Graphical Modeling Vial1Penalization

Biometrics ◽  
2012 ◽  
Vol 68 (4) ◽  
pp. 1197-1206 ◽  
Author(s):  
Hokeun Sun ◽  
Hongzhe Li
Author(s):  
Laura Codazzi ◽  
Alessandro Colombi ◽  
Matteo Gianella ◽  
Raffaele Argiento ◽  
Lucia Paci ◽  
...  

2017 ◽  
Vol 161 ◽  
pp. 172-190 ◽  
Author(s):  
Kei Hirose ◽  
Hironori Fujisawa ◽  
Jun Sese

2011 ◽  
Vol 5 (1) ◽  
pp. 21 ◽  
Author(s):  
Jan Krumsiek ◽  
Karsten Suhre ◽  
Thomas Illig ◽  
Jerzy Adamski ◽  
Fabian J Theis

2006 ◽  
Vol 97 (7) ◽  
pp. 1525-1550 ◽  
Author(s):  
Masashi Miyamura ◽  
Yutaka Kano

2011 ◽  
Author(s):  
Nikola S. Mueller ◽  
Jan Krumsiek ◽  
Fabian J. Theis ◽  
Christian Böhm ◽  
Anke Meyer-Bäse

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yunqi Bu ◽  
Johannes Lederer

Abstract Graphical models such as brain connectomes derived from functional magnetic resonance imaging (fMRI) data are considered a prime gateway to understanding network-type processes. We show, however, that standard methods for graphical modeling can fail to provide accurate graph recovery even with optimal tuning and large sample sizes. We attempt to solve this problem by leveraging information that is often readily available in practice but neglected, such as the spatial positions of the measurements. This information is incorporated into the tuning parameter of neighborhood selection, for example, in the form of pairwise distances. Our approach is computationally convenient and efficient, carries a clear Bayesian interpretation, and improves standard methods in terms of statistical stability. Applied to data about Alzheimer’s disease, our approach allows us to highlight the central role of lobes in the connectivity structure of the brain and to identify an increased connectivity within the cerebellum for Alzheimer’s patients compared to other subjects.


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