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CATEGORIES:Colloquium/Seminar
DESCRIPTION:Bailey Fosdisk\, Department of Statistics\, Colorado State Univ
 ersity\n\nInferring latent networks from longitudinal relational data\n\nLo
 ngitudinal bipartite relational data characterize the evolution of relation
 s between pairs of actors\, where actors are of two distinct types and rela
 tions exist only between disparate types. A common goal is to understand th
 e temporal dependencies\, specifically which actor relations incite later a
 ctor relations. There are two existing approaches to this problem. The firs
 t approach projects the bipartite data in each time period to a unipartite 
 network and uses existing unipartite network models. Unfortunately\, inform
 ation is lost in calculating the projection and generative models for netwo
 rks obtained through this process are scarce. The second approach represent
 s dependencies using two unipartite influence networks\, corresponding to t
 he two actor types. Existing models taking this approach are bilinear in th
 e influence networks\, creating challenges in computation and interpretatio
 n. We propose a novel generative model that permits estimation of weighted\
 , directed influence networks and does not suffer from these shortcomings. 
 The proposed model is linear in the influence networks\, permitting inferen
 ce using off-the-shelf software tools. We prove our estimator is consistent
  under cases of model misspecification. We demonstrate the performance of t
 he proposed model in simulation studies and an analysis of weekly internati
 onal state interactions.
DTEND:20181108T220000Z
DTSTAMP:20260314T060731Z
DTSTART:20181108T210000Z
GEO:40.006791;-105.262818
LOCATION:Engineering Center\, ECOT 226
SEQUENCE:0
SUMMARY:Complex/Dynamical Systems Seminar - Bailey Fosdick
UID:tag:localist.com\,2008:EventInstance_3883016
URL:https://calendar.colorado.edu/event/complexdynamical_systems_seminar_-_
 natasha_bosanac
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