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Home | Seminars and Symposia | Past seminars/symposia: Tuesday, April 9, 2013

DTC Seminar Series

Network Granger Causality with Inherent Grouping Structure


George Michailidis
University of Michigan

Tuesday, April 9, 2013
3:30 p.m. reception
4:00 p.m. seminar

401/402 Walter Library

View webcast of this seminar

MichailidisThe problem of estimating high-dimensional network models arises naturally in the analysis of many biological and socio-economic systems. In this work, we aim to learn a network structure from temporal panel data, employing the framework of Granger causal models under the assumptions of sparsity of its edges and inherent grouping structure among its nodes. To that end, we introduce a group lasso regression regularization framework, and also examine a thresholded variant to address the issue of group misspecification. Further, the norm consistency and variable selection consistency of the estimates is established, the latter under the novel concept of direction consistency. The performance of the proposed methodology is assessed through an extensive set of simulation studies and comparisons with existing techniques. The study is illustrated on two motivating examples coming from functional genomics and financial econometrics.


George Michailidis received his Ph.D. in Mathematics from UCLA in 1996 and then did a postdoc in Operations Research at Stanford University. He joined the University of Michigan in 1998, where he is currently a Professor of Statistics and EECS. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association, the Editor in Chief of the Electronic Journal of Statistics and has served on the editorial board of many statistics journals. He has repeatedly been a member of the technical program committee for many conferences including, IEEE SmartGrid Comm, ICC, Globecom and ACM KDD. His research interests are in the areas of stochastic network modeling and performance evaluation, queuing analysis and congestion control, statistical modeling and analysis of Internet traffic, network tomography, and analysis of high dimensional data with network structure.