Vector Autoregressive based Network Models
1 hour 8 mins,
262.53 MB,
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480x270,
29.97 fps,
44100 Hz,
527.12 kbits/sec
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About this item
| Description: |
Michailidis, G (University of Florida)
Friday 16th December 2016 - 09:30 to 10:30 |
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| Created: | 2016-12-21 10:51 |
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| Collection: | Theoretical Foundations for Statistical Network Analysis |
| Publisher: | Isaac Newton Institute |
| Copyright: | Michailidis, G |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | 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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| iPod Video * | 480x270 | 527.12 kbits/sec | 262.53 MB | View | Download | |
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