
FuDGE: Functional Differential Graph Estimation with fully and discretely observed curves
We consider the problem of estimating the difference between two functio...
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Nonparametric and highdimensional functional graphical models
We consider the problem of constructing nonparametric undirected graphic...
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Graph Estimation From Multiattribute Data
Many real world network problems often concern multivariate nodal attrib...
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RegionReferenced Spectral Power Dynamics of EEG Signals: A Hierarchical Modeling Approach
Functional brain imaging through electroencephalography (EEG) relies upo...
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Extreme Graphical Models with Applications to Functional Neuronal Connectivity
With modern calcium imaging technology, the activities of thousands of n...
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Direct estimation of differential Granger causality between two highdimensional time series
Differential Granger causality, that is understanding how Granger causal...
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ACE of Space: Estimating Genetic Components of HighDimensional Imaging Data
It is of great interest to quantify the contributions of genetic variati...
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Direct Estimation of Differential Functional Graphical Models
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 highdimensional 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 highdimensional 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.
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