High-Dimensional Covariate-Dependent Gaussian Graphical Models
提出一种协变量依赖的高斯图模型，通过新参数化方法捕捉随协变量变化的网络结构，并开发统计推断和稀疏估计方法，应用于流感疫苗和唐氏综合征数据集。
Motivated by dynamic biologic network analysis, we propose a covariate-dependent Gaussian graphical model (cdexGGM) for capturing network structure that varies with covariates through a novel parameterization. Using a likelihood framework, our methodology jointly estimates all dynamic edge and vertex parameters. We further develop statistical inference procedures to test the dynamic nature of the underlying network. Concerning large-scale networks, we perform composite likelihood estimation with an l1 penalty to discover sparse dynamic network structures. We establish the estimation error bound in l2 norm and validate the sign consistency in the high-dimensional context. We apply our method to an influenza vaccine dataset to model the dynamic gene network that evolves with time. We also investigate a Down syndrome dataset to model the dynamic protein network which varies under a factorial experimental design. These applications demonstrate the applicability and effectiveness of the proposed model. Supplementary materials for this article are available online.