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UID:20250713T044027EDT-4123Up57El@132.216.98.100
DTSTAMP:20250713T084027Z
DESCRIPTION:Estimation and inference for network contagion\n\nWe present tw
 o problems involving statistical inference and estimation for stochastic s
 preading over a fixed network. The first problem concerns hypothesis testi
 ng for the underlying graph over which the disease is spreading\, when we 
 only observe the infection states of individual nodes after a single epide
 mic outbreak. We present a permutation test that is valid under appropriat
 e conditions on the homogeneity of the spreading parameters and assumption
 s regarding the symmetry groups of the graphs involved in the null and alt
 ernative hypotheses. The second problem concerns parameter estimation for 
 a similar type of contagion model\, which incorporates covariate informati
 on on each of the edges of the graph. In this setting\, we assume the stru
 cture of the graph is known\, and we also know the order in which nodes co
 ntract the disease from their infected neighbors. We derive consistency an
 d asymptotic normality of the maximum likelihood estimator\, which may be 
 obtained via convex optimization.\n	\n	This is joint work with Justin Khim (
 UPenn).\n
DTSTART:20180924T180000Z
DTEND:20180924T190000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Po-Ling Lo\, Unversity of Wisconsin-Madison
URL:/mathstat/channels/event/po-ling-lo-unversity-wisc
 onsin-madison-289918
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