Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model
协变量调整高斯图模型的渐近正态与有效估计
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…