Statistics and Data Science Seminar: Likelihood inference for a large causal network, by Xiaotong Shen
March 11, 2020
3:00 PM - 3:50 PM
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Xiaotong Shen (University of Minnesota ): Likelihood inference for a large causal network
Inference of causal relations between interacting units
in a directed acyclic graph (DAG), such as a regulatory gene network, is common
in practice, imposing challenges because of a lack of inferential tools.
In this talk, I will present 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 connectivity, the
asymptotic distribution is either chi-squared or normal depending 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
independent chi-squared variables with one-degree
of freedom or a generalized Gamma distribution depending on if d is
small, where d is the number of breakpoints in a hypothesized pathway.
Computational methods will be discussed, 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 University of Minnesota.
Feb 18, 2020
Feb 18, 2020