Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model
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. …