|Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model.
|Year of Publication
|Chen, Mengjie, Zhao Ren, Hongyu Zhao, and Harrison Zhou
|J Am Stat Assoc
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.
|J Am Stat Assoc
|Asymptotically normal and efficient estimation of covariate-adjusted Gaussian graphical model.
|PubMed Central ID
|R01 CA082659 / CA / NCI NIH HHS / United States
S10 RR029676 / RR / NCRR NIH HHS / United States
UL1 TR001863 / TR / NCATS NIH HHS / United States
S10 RR019895 / RR / NCRR NIH HHS / United States
P01 CA154295 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30 CA016359 / CA / NCI NIH HHS / United States
R01 GM059507 / GM / NIGMS NIH HHS / United States
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model.