Title | Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Chen, Mengjie, Zhao Ren, Hongyu Zhao, and Harrison Zhou |
Journal | J Am Stat Assoc |
Volume | 111 |
Issue | 513 |
Pagination | 394-406 |
Date Published | 2016 Mar |
ISSN | 0162-1459 |
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. |
DOI | 10.1080/01621459.2015.1010039 |
Alternate Journal | J Am Stat Assoc |
Original Publication | Asymptotically normal and efficient estimation of covariate-adjusted Gaussian graphical model. |
PubMed ID | 27499564 |
PubMed Central ID | PMC4974017 |
Grant List | 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.
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