Direct Estimation of Differential Functional Graphical Models

Autor:	Zhao, Boxin, Wang, Y. Samuel, Kolar, Mladen
Rok vydání:	2019
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: 21 pages, 3 figures, to be published in NeurIPS 2019; added link to code
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.09701 Zobrazit plný text záznamu View this record from Arxiv