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X-WR-CALNAME:Department of Statistics
X-ORIGINAL-URL:https://stat.wisc.edu
X-WR-CALDESC:Events for Department of Statistics
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TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20200308T080000
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DTSTART:20201101T070000
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DTSTART;TZID=America/Chicago:20200129T160000
DTEND;TZID=America/Chicago:20200129T170000
DTSTAMP:20210301T081016
CREATED:20200113T170915Z
LAST-MODIFIED:20200127T141837Z
UID:2726-1580313600-1580317200@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: Bootstrapping Networks with Latent Geometric Structure \nPresenter: Keith Levin \nAbstract: A core problem in statistical network analysis is to develop network analogues of classical statistical techniques. The problem of bootstrapping network data stands out as especially challenging\, owing to the dependency structure of network data and the fact that one typically observes only a single network\, rather than a sample. We propose two methods for obtaining bootstrap samples for networks drawn from latent space models\, a class of network models in which unobserved geometric structure drives network topology. The first of these two bootstrap methods leverages the structure of these models to generate bootstrap samples of whole networks. The second method generates bootstrap samples of network statistics that are expressible as U-statistics in the latent geometry\, a class of functions that includes subgraph densities and a number of other useful network summaries. We prove the consistency of both of the proposed bootstrap methods under the random dot product graph\, a latent space model that includes the popular stochastic blockmodel as a special case\, though our methods are applicable to any latent space model in which the latent geometry can be recovered suitably accurately. If time allows\, we will briefly discuss ongoing work applying these new bootstrap techniques to problems in neuroimaging.
URL:https://stat.wisc.edu/event/statistics-seminar-4-2020-01-29/
LOCATION:133 SMI
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