Structure learning of exponential family graphical model with false discovery rate control
Structure learning of exponential family graphical model with false discovery rate control
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DOI：
10.1007/s42952-023-00213-8
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发表时间：
2023-05
影响因子：
0.6
通讯作者：
Yanhong Liu;Yuhao Zhang;Zhonghua Li
中科院分区：
文献类型：
--
作者：
Yanhong Liu;Yuhao Zhang;Zhonghua Li
概率图模型由于能够模拟随机变量之间的条件依赖关系而在广泛的领域中受到欢迎。研究了具有错误发现率控制的指数族图模型的结构学习问题。大多数现有的FDR控制的结构学习程序已被设计为高斯图形模型（GGM）。更一般的指数族图模型的系统方法仍然缺乏。在本文中，我们介绍了一个统一的程序来学习的指数族图形模型的结构与FDR控制利用对称化数据聚合（SDA）技术，通过样本分裂，数据筛选，和信息池。我们表明，我们的方法控制FDR渐近在一些温和的条件下。大量的仿真结果和两个实际数据的例子验证了我们的方法的有效性。
Probabilistic graphical models enjoy great popularity in a wide range of domains due to their ability to model the conditional dependency relationships among random variables. This paper explores the structure learning for the exponential family graphical model with false discovery rate (FDR) control. Most existing FDR-controlled structure learning procedures have been designed for the Gaussian graphical model (GGM). A systematic approach for more general exponential family graphical models is still lacking. In this paper, we introduce a unified procedure to learn the structure of the exponential family graphical model with FDR control utilizing the symmetrized data aggregation (SDA) technique via sample splitting, data screening, and information pooling. We show that our method controls FDR asymptotically under some mild conditions. Extensive simulation results and two real-data examples validate the effectiveness of our method.