ArticleJournal of the American Statistical Association2020
Likelihood ratio tests for a large directed acyclic graph.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning.Journal of the American Statistical Association · 2024Article
- Discovery and inference of a causal network with hidden confounding.Journal of the American Statistical Association · 2024Article
- Nonlinear causal discovery with confounders.Journal of the American Statistical Association · 2024Article
- Functional Structural Equation Model.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
- Penalized mediation models for multivariate data.Genetic epidemiology · 2022Article
- Testing Mediation Effects Using Logic of Boolean Matrices.Journal of the American Statistical Association · 2022Article
- Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data.PLoS genetics · 2020Article
- A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models.Journal of machine learning research : JMLR · 2020Article
- Inference for a Large Directed Acyclic Graph with Unspecified Interventions.Journal of machine learning research : JMLRArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Inference of directional pairwise relations between interacting units in a directed acyclic graph (DAG), such as a regulatory gene network, is common in practice, imposing challenges because of lack of inferential tools. For example, inferring a specific gene pathway of a regulatory gene network is biologically important. Yet, frequentist inference of directionality of connections remains largely unexplored for regulatory models. In this article, we propose 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, we derive the asymptotic distributions of the constrained likelihood ratios in a high-dimensional situation. 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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.