Simultaneous Inference in Multiple Matrix-Variate Graphs for High-Dimensional Neural Recordings

TL;DR

This paper introduces a new statistical framework for simultaneous inference on multiple high-dimensional matrix-variate Gaussian graphical models, effectively handling temporal dependence and heterogeneity across groups.

Contribution

It proposes a unified, computationally feasible method combining joint estimation and bootstrap to test graph edges across multiple groups with theoretical guarantees.

Findings

Method performs well in simulations.

Successfully applied to neural recording data.

Achieves near-optimal testing boundaries.

Abstract

We study simultaneous inference for multiple matrix-variate Gaussian graphical models in high-dimensional settings. Such models arise when spatiotemporal data are collected across multiple sample groups or experimental sessions, where each group is characterized by its own graphical structure but shares common sparsity patterns. A central challenge is to conduct valid inference on collections of graph edges while efficiently borrowing strength across groups under both high-dimensionality and temporal dependence. We propose a unified framework that combines joint estimation via group penalized regression with a high-dimensional Gaussian approximation bootstrap to enable global testing of edge subsets of arbitrary size. The proposed procedure accommodates temporally dependent observations and avoids naive pooling across heterogeneous groups. We establish theoretical guarantees for the validity of the simultaneous tests under mild conditions on sample size, dimensionality, and non-stationary autoregressive temporal dependence, and show that the resulting tests are nearly optimal in terms of the testable region boundary. The method relies only on convex optimization and parametric bootstrap, making it computationally tractable. Simulation studies and a neural recording example illustrate the efficacy of the proposed approach.