Mar 11 2020

Statistics and Data Science Seminar: Likelihood inference for a large causal network, by Xiaotong Shen

March 11, 2020

3:00 PM - 3:50 PM

Location

636 SEO

Address

Chicago, IL

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.

Please click here to make changes to, or delete, this seminar announcement.

Contact

Yichao Wu

Date posted

Feb 18, 2020

Date updated

Feb 18, 2020

Speakers

Xiaotong Shen | (University of Minnesota )