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Activity Number: 251
Type: Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #312957
Title: Sparse Structural Factor Equation Models and Its Applications to Gene Regulatory Network Inference
Author(s): Yan Zhou*+ and Peter Song and Xiaoquan Wen
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: acyclic directed mixed graphs ; structural equation model ; latent factors ; regularization
Abstract:

Using directed acyclic graphs (DAGs) to represent causal relationships among random variables is very common in graphical models. When the nodes exhibit a natural ordering, the problem of estimating directed graphs reduces to the problem of estimating the structure of the network. Structural equation model (SEM) is a very powerful tool for the modeling of DAGs that describe causal relationships. However, deriving the topology of DAG from the data can become even harder in practice, because such causal relationship may be obscured by some unobserved factors. Thus, in general, the network is a mixed graph containing both directed and undirected edges. We propose a new methodology--sparse structural factor equation modeling (SFEM) framework, in which we can construct DAGs while accounting for potential latent factors. Conditional on such latent factors, we are able to remove and clean up non-directed edges in acyclic directed mixed graphs(ADMGs), so that we obtain a simpler and more interpretable topology of the causal network. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies, as well as real, genetical genomics data.


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