Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications

TL;DR

This paper introduces FLAG, a novel method for flexible, accurate estimation and inference of Gaussian graphical models that does not require sparsity assumptions and is computationally efficient, with applications in diverse real-world data.

Contribution

The paper presents FLAG, a new approach for GGM estimation that allows element-wise inference, handles multiple graphs jointly, and improves computational efficiency over existing methods.

Findings

FLAG achieves accurate graph recovery in simulations.

FLAG performs well on real data applications.

The method is computationally efficient and flexible.

Abstract

The Gaussian graphical model (GGM) incorporates an undirected graph to represent the conditional dependence between variables, with the precision matrix encoding partial correlation between pair of variables given the others. To achieve flexible and accurate estimation and inference of GGM, we propose the novel method FLAG, which utilizes the random effects model for pairwise conditional regression to estimate the precision matrix and applies statistical tests to recover the graph. Compared with existing methods, FLAG has several unique advantages: (i) it provides accurate estimation without sparsity assumptions on the precision matrix, (ii) it allows for element-wise inference of the precision matrix, (iii) it achieves computational efficiency by developing an efficient PX-EM algorithm and a MM algorithm accelerated with low-rank updates, and (iv) it enables joint estimation of multiple graphs using FLAG-Meta or FLAG-CA. The proposed methods are evaluated using various simulation settings and real data applications, including gene expression in the human brain, term association in university websites, and stock prices in the U.S. financial market. The results demonstrate that FLAG and its extensions provide accurate precision estimation and graph recovery.