Supervised Heterogeneous Gaussian Graphical Models
提出一种监督异质高斯图模型，同时处理连续和离散响应变量，用高维ECM算法估计，并通过光谱数据分析展示其优越性能。
Mixture regression models are widely employed for analyzing heterogeneous data. Currently, network-based heterogeneity analysis methods predominantly focus on unsupervised learning, intending to uncover subgroup structures within data and estimate multiple graphs/networks. However, in many real-world scenarios, there is a significant emphasis on both prediction performance of the model and meaningful interpretation of the identified subgroups. In this study, we propose a supervised heterogeneous Gaussian graphical model that accommodates both continuous and discrete response variables. A high-dimensional expectation-conditional-maximization (ECM) algorithm is developed for estimation. We provide a non-asymptotic statistical analysis of the outputs generated from the ECM algorithm. Additionally, numerical studies are conducted to demonstrate the superior performance of our approach, which is further illustrated through an analysis of spectrometric data. Supplementary materials are available online.