Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning
Year: 2023
Type: article
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.
Authors: Chengchun Shi, Yunzhe Zhou, Lexin Li
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Statistical Methods in Offline Reinforcement Learning, New Statistical Methods for Multicenter Multimodal Longitudinal Neuroimaging Analysis, Mechanisms of Alzheimer's Disease Progression in the Aging Brain, CIF: Small: Collaborative Research: Graphical Modeling of Multivariate Functional Data, G8406896559, G8659321454
OpenAlex ID: W4378802386