BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20250804T051608EDT-3816TXDEcw@132.216.98.100
DTSTAMP:20250804T091608Z
DESCRIPTION:Title: Linear Regression and its Inference on Noisy Network-lin
 ked Data\n\n\n	Abstract:\n\n\nLinear regression on a set of observations li
 nked by a network has been an essential tool in modeling the relationship 
 between response and covariates with additional network data. Despite its 
 wide range of applications in many areas\, such as social sciences and hea
 lth-related research\, the problem has not been well-studied in statistics
  so far. Previous methods either lack of inference tools or rely on restri
 ctive assumptions on social effects\, and usually treat the network struct
 ure as precisely observed\, which is too good to be true in many problems.
  We propose a linear regression model with nonparametric social effects. O
 ur model does not assume the relational data or network structure to be ac
 curately observed\; thus\, our method can be provably robust to a certain 
 level of perturbation of the network structure. We establish a full set of
  computationally efficient asymptotic inference tools under a general requ
 irement of the perturbation and then study the robustness of our method in
  the specific setting when the perturbation is from random network models.
  We discover a phase-transition phenomenon of inference validity concernin
 g the network density when no prior knowledge about the network model is a
 vailable\, while also show the significant improvement achieved by knowing
  the network model. A by-product of our analysis is a rate-optimal concent
 ration bound about subspace projection that may be of independent interest
 . We conduct extensive simulation studies to verify our theoretical observ
 ations and demonstrate the advantage of our method compared to a few bench
 marks under different data-generating models. The method is then applied t
 o adolescent network data to study the gender and racial differences in so
 cial activities.\n\n\n	Speaker\n\n\nTianxi Li is an assistant professor in 
 the Department of Statistics at the University of Virginia. His research i
 s mainly about statistical network analysis and statistical learning. He o
 btained his PhD in statistics from the University of Michigan in 2018.\n\n
 Zoom Link\n\nMeeting ID: 924 5390 4989\n\nPasscode: 690084\n
DTSTART:20201023T193000Z
DTEND:20201023T203000Z
SUMMARY:Tianxi Li (University of Virginia)
URL:/mathstat/channels/event/tianxi-li-university-virg
 inia-325551
END:VEVENT
END:VCALENDAR