Graphical Model Inference with Erosely Measured Data

TL;DR

This paper introduces GI-JOE, a novel inference method for Gaussian graphical models with irregular, erosely measured data, providing edge-wise uncertainty quantification and FDR control, validated through simulations and neuroscience data.

Contribution

It develops the first inference approach tailored for erosely measured data, accounting for variable sample sizes across node pairs in Gaussian graphical models.

Findings

Effective edge-wise inference with FDR control in erosely measured data

Statistical validity proven for irregular measurement settings

Improved graph selection demonstrated in neuroscience data

Abstract

In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Existing works characterize the graph selection performance using the minimum pairwise sample size, which provides little insights for erosely measured data, and no existing inference method is applicable. We aim to fill in this gap by proposing the first inference method that characterizes the different uncertainty levels over the graph caused by the erose measurements, named GI-JOE (Graph Inference when Joint Observations are Erose). Specifically, we develop an edge-wise inference method and an affiliated FDR control procedure, where the variance of each edge depends on the sample sizes associated with corresponding neighbors. We prove statistical validity under erose measurements, thanks to careful localized edge-wise analysis and disentangling the dependencies across the graph. Finally, through simulation studies and a real neuroscience data example, we demonstrate the advantages of our inference methods for graph selection from erosely measured data.