ArticleJournal of the American Statistical Association2016
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer's disease and APOE-Computational statistics & data analysis · 2026Article
- Statistical inference on high-dimensional covariate-dependent Gaussian graphical regressions.Biometrics · 2025Article
- Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Conditional Gaussian graphical model for estimating personalized disease symptom networks.Statistics in medicine · 2022Article
- Scalable network estimation withStatistical analysis and data mining · 2021Article
- Bayesian Structure Learning in Multi-layered Genomic Networks.Journal of the American Statistical Association · 2021Article
- Identifying disease-associated biomarker network features through conditional graphical model.Biometrics · 2020Article
- RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs.Journal of the American Statistical Association · 2020Article
- Geospatial analysis and assessment ofEnvironmental monitoring and assessment · 2019Article
- False discovery rate control for high dimensional networks of quantile associations conditioning on covariates.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2018Article
- FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.PLoS computational biology · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A tuning-free procedure is proposed to estimate the covariate-adjusted Gaussian graphical model. For each finite subgraph, this estimator is asymptotically normal and efficient. As a consequence, a confidence interval can be obtained for each edge. The procedure enjoys easy implementation and efficient computation through parallel estimation on subgraphs or edges. We further apply the asymptotic normality result to perform support recovery through edge-wise adaptive thresholding. This support recovery procedure is called ANTAC, standing for Asymptotically Normal estimation with Thresholding after Adjusting Covariates. ANTAC outperforms other methodologies in the literature in a range of simulation studies. We apply ANTAC to identify gene-gene interactions using an eQTL dataset. Our result achieves better interpretability and accuracy in comparison with CAMPE.
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Registered trials
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.