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Vector Autoregressive based Network Models

Presented by: 
George Michailidis
Friday 16th December 2016 - 09:30 to 10:30
INI Seminar Room 1
Vector autoregressions represent a popular class of time series models that aim to capture temporal interconnections between temporally evolving
entities.  They have been widely used in macroeconomic and financial modeling and more recently they have found novel applications in functional genomics and neuroscience. In this presentation, we discuss modeling and estimation issues in the high dimensional setting under different constrains
on the transition matrices - sparsity, low rankness. We also provide extensions to multi-layer networks and illustrate the results with application​s
to financial stability monitoring and biological regulation.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons