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SUMMARY:Xiaotong Shen (University of Minnesota)
DTSTART:20201228T013000Z
DTEND:20201228T023000Z
DTSTAMP:20260423T024737Z
UID:iccm2020/33
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/iccm2020/33/
 ">Likelihood inference for a large causal network</a>\nby Xiaotong Shen (U
 niversity of Minnesota) as part of ICCM 2020\n\n\nAbstract\nInference of c
 ausal relations between interacting units in a directed acyclic graph (DAG
 )\, such as a regulatory gene network\, is common in practice\, imposing c
 hallenges because of a lack of inferential tools.In this talk\, I will pre
 sent constrained likelihood ratio tests for inference of the connectivity 
 as well as directionality subject to nonconvex acyclicity constraints in a
  Gaussian directed graphical model. Particularly\,for testing of connectiv
 ity\, the asymptotic distribution is either chi-squared or normal dependin
 g on if the number of testable links in a DAG model is small\; for testing
  of directionality\, the asymptotic distribution is the minimum of d indep
 endent chi-squared variables with one-degree of freedom or a generalized G
 amma distribution depending on if d is small\, where d is the number of br
 eakpoints in a hypothesized pathway.Computational methods will be discusse
 d\, in addition to some numerical examples to infer a directed pathway in 
 a gene network. This work is joint with Chunlin Li and Wei Pan of the Univ
 ersity of Minnesota.\n
LOCATION:https://researchseminars.org/talk/iccm2020/33/
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