High-Dimensional Inference for Cluster-Based Graphical Models

Motivated by modern applications in which one constructs graphical models based on a very large number of features, this paper introduces a new class of cluster-based graphical models. Unlike standard graphical models, variable clustering is applied as an initial step for reducing the dimension of the feature space. We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable. Two different cluster-based Gaussian graphical models are considered: the latent variable graph, corresponding to the graphical model associated with the unobserved latent variables, and the cluster-average graph, corresponding to the vector of features averaged over clusters. We derive estimates tailored to these graphs, with the goal of pattern recovery under false discovery rate (FDR) control. Our study reveals that likelihood based inference for the latent graph is analytically intractable, and we develop alternative estimation and inference strategies. We replace the likelihood of the data by appropriate empirical risk functions that allow for valid inference in both graphical models under study. Our main results are Berry-Esseen central limit theorems for the proposed estimators, which are proved under weaker assumptions than those employed in the existing literature on Gaussian graphical model inference. We make explicit the implications of the asymptotic approximations on graph recovery under FDR control, and show when it can be controlled asymptotically. Our analysis takes into account the uncertainty induced by the initial clustering step. We find that the errors induced by clustering are asymptotically ignorable in the follow-up analysis, under no further restrictions on the parameter space for which inference is valid. The theoretical properties of the proposed procedures are verified on simulated data and an fMRI data analysis.