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SUMMARY:Mixed Effects Model on Functional Manifolds / Sampling Directed Ne
 tworks - Jingjing Zou (University of Cambridge)
DTSTART:20180322T090000Z
DTEND:20180322T100000Z
UID:TALK102790@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:I would like to talk about two projects. Co-authors of Mixed E
 ffects Model on Functional Manifolds: John Aston (University of Cambridge)
 \, Lexin Li (UC Berkeley) We propose a generalized mixed effects model to 
 study effects of subject-specific covariates on geometric and functional f
 eatures of the subjects&#39\; surfaces. Here the covariates include both t
 ime-invariant covariates which affect both the geometric and functional fe
 atures\, and time-varying covariates which result in longitudinal changes 
 in the functional textures. In addition\, we extend the usual mixed effect
 s model to model the covariance between a subject&#39\;s geometric deforma
 tion and functional textures on the surface.   Co-authors of Sampling Dire
 cted Networks: Richard Davis (Columbia University)\, Gennady Samorodnitsky
  (Cornell University)\, Zhi-Li Zhang (University of Minnesota).  We propos
 e a sampling procedure for the nodes in a network with the goal of estimat
 ing uncommon population features of the entire network.  Such features mig
 ht include tail behavior of the in-degree and out-degree distributions and
  as well as their joint distribution.  Our procedure is based on selecting
  random initial nodes and then following the path of linked nodes in a str
 uctured fashion.  In this procedure\, targeted nodes with desired features
 \, such as large in-degree\, will have a larger probability of being retai
 ned.  In order to construct nearly unbiased estimates of the quantities of
  interest\, weights associated with the sampled nodes must be calculated. 
  We will illustrate this procedure and compare it with a sampling scheme b
 ased on multiple random walks on several data sets including webpage netwo
 rk data and Google+ social network data.
LOCATION:Seminar Room 1\, Newton Institute
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