通过邻域筛选实现高斯图模型的错误发现率控制
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
AI总结：
本文提出一种无需调参的逐节点变量选择图学习方法，通过邻域融合控制高斯图模型边选择的错误发现率，并在多种图拓扑实验中显著优于现有方法。
摘要：
高斯图模型广泛应用于众多领域。它们将变量之间的统计关系建模为图，其中两个变量之间的边表示条件依赖。遗憾的是，诸如 graphical lasso 或邻域选择等成熟估计方法已知容易产生大量虚假边检测。虚假检测可能导致不准确甚至错误的科学解释，在生物医学或医疗健康等应用中具有重大影响。本文提出一种逐节点变量选择方法用于图学习，并证明所选边集的错误发现率可在自估计水平上得到控制。一种新颖的个体邻域融合方法输出无向图估计。所提方法无需参数，不需要用户调参。在考虑不同图拓扑的数值实验中，与竞争性错误发现率控制方法进行基准比较，结果显示性能有显著提升。
英文摘要：
Gaussian graphical models emerge in a wide range of fields. They model the statistical relationships between variables as a graph, where an edge between two variables indicates conditional dependence. Unfortunately, well-established estimators, such as the graphical lasso or neighborhood selection, are known to be susceptible to a high prevalence of false edge detections. False detections may encourage inaccurate or even incorrect scientific interpretations, with major implications in applications, such as biomedicine or healthcare. In this paper, we introduce a nodewise variable selection approach to graph learning and provably control the false discovery rate of the selected edge set at a self-estimated level. A novel fusion method of the individual neighborhoods outputs an undirected graph estimate. The proposed method is parameter-free and does not require tuning by the user. Benchmarks against competing false discovery rate controlling methods in numerical experiments considering different graph topologies show a significant gain in performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
