Testing the Graph of a Gaussian Graphical Model

TL;DR

This paper introduces a new, computationally simple goodness-of-fit test for Gaussian graphical models, with a known asymptotic distribution, that improves model validation in high-dimensional data analysis.

Contribution

It proposes a novel Gumbel-distribution-based test for the graph structure, including a consistency-empowered version for nested models, enhancing model validation tools.

Findings

Test maintains correct size under null hypothesis.

Test demonstrates high power against alternatives.

Application to COVID-19 data shows practical utility.

Abstract

The Gaussian graphical model is routinely employed to model the joint distribution of multiple random variables. The graph it induces is not only useful for describing the relationship between random variables but also critical for improving statistical estimation precision. In high-dimensional data analysis, despite an abundant literature on estimating this graph structure, tests for the adequacy of its specification at a global level is severely underdeveloped. To make progress, this paper proposes a novel goodness-of-fit test that is computationally easy and theoretically tractable. Under the null hypothesis, it is shown that asymptotic distribution of the proposed test statistic follows a Gumbel distribution. Interestingly the location parameter of this limiting Gumbel distribution depends on the dependence structure under the null. We further develop a novel consistency-empowered test statistic when the true structure is nested in the postulated structure, by amplifying the noise incurred in estimation. Extensive simulation illustrates that the proposed test procedure has the right size under the null, and is powerful under the alternative. As an application, we apply the test to the analysis of a COVID-19 data set, demonstrating that our test can serve as a valuable tool in choosing a graph structure to improve estimation efficiency.