title: Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model.
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.
authors: Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
authors: Mengjie
authors: M
authors: Chen
authors: Department of Statistics, Yale University, New Haven, CT 06520, USA.
authors: Zhao
authors: Z
authors: Ren
authors: Department of Biostatistics, School of Public Health, Yale University, New Haven, CT 06520, USA.
authors: Hongyu
authors: H
authors: Zhao
authors: Department of Statistics, Yale University, New Haven, CT 06520, USA.
authors: Harrison
authors: H
authors: Zhou
keywords: Gene regulatory network; High-dimensional statistics; Precision matrix estimation; Sparsity; Support recovery; eQTL