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Direct Estimation of Differential Functional Graphical Models
Boxin Zhao · Y. Samuel Wang · Mladen Kolar

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #180

We consider the problem of estimating the difference between two functional undirected graphical models with shared structures. In many applications, data are naturally regarded as high-dimensional random function vectors rather than multivariate scalars. For example, electroencephalography (EEG) data are more appropriately treated as functions of time. In these problems, not only can the number of functions measured per sample be large, but each function is itself an infinite dimensional object, making estimation of model parameters challenging. We develop a method that directly estimates the difference of graphs, avoiding separate estimation of each graph, and show it is consistent in certain high-dimensional settings. We illustrate finite sample properties of our method through simulation studies. Finally, we apply our method to EEG data to uncover differences in functional brain connectivity between alcoholics and control subjects.

Author Information

Boxin Zhao (UChicago)

2018.09 - Now master student, Department of Statistics, University of Chicago 2014.09 - 2018.09 undergraduate student, School of Mathematical Science, Nankai University

Sam Wang (U of Chicago)
Mladen Kolar (University of Chicago)

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