Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning

• 成果类型: Article
• 署名作者: Shi, Chengchun; Zhou, Yunzhe; Li, Lexin
• 署名单位: University of London; London School Economics & Political Science; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2220169
• 发表日期: 2024
• 页码: 1833-1846
• 关键词: deep neural-networks; brain networks; connectivity; likelihood; DISCOVERY; models; VALUES

摘要

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.