Testing directed acyclic graph via structural, supervised and generative adversarial learning

Shi, Chengchun ORCID: 0000-0001-7773-2099, Zhou, Yunzhe and Li, Lexin (2023) Testing directed acyclic graph via structural, supervised and generative adversarial learning. Journal of the American Statistical Association. ISSN 0162-1459

Text (Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Networks) - Accepted Version Download (1MB)

• Author
• Scopus publication

Abstract

In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods often impose some specific model structures such as linear models or additive models, and assume independent data observations. Our proposed test instead allows the associations among the random variables to be nonlinear and the data to be time-dependent. We build the test based on some highly flexible neural networks learners. We establish the asymptotic guarantees of the test, while allowing either the number of subjects or the number of time points for each subject to diverge to infinity. We demonstrate the efficacy of the test through simulations and a brain connectivity network analysis. Supplementary materials for this article are available online.

Item Type:	Article
Official URL:	https://www.tandfonline.com/toc/uasa20
Additional Information:	© 2023 American Statistical Association
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	19 Jun 2023 15:42
Last Modified:	11 Nov 2024 18:48
URI:	http://eprints.lse.ac.uk/id/eprint/119446

Actions (login required)

View Item