Comparison of statistical procedures for Gaussian graphical model selection

TL;DR

This paper compares various statistical procedures for Gaussian graphical model selection, evaluating their performance using measures like Type I/II errors and ROC AUC to identify the most effective methods for different scenarios.

Contribution

It provides a systematic comparison of multiple procedures for Gaussian graphical model selection, highlighting their strengths and weaknesses.

Findings

Different procedures vary significantly in error rates and ROC AUC.

Some methods outperform others depending on the specific measure used.

The study guides choosing appropriate procedures based on application needs.

Abstract

Graphical models are used in a variety of problems to uncover hidden structures. There is a huge number of different identification procedures, constructed for different purposes. However, it is important to research different properties of such procedures and compare them in order to find out the best procedure or the best use case for some specific procedure. In this paper, some statistical identification procedures are compared using different measures, such as Type I and Type II errors, ROC AUC.