Reproducible Learning of Gaussian Graphical Models via Graphical Lasso Multiple Data Splitting

IF 0.8 3区 数学 Q2 MATHEMATICS
Kang Hu, Danning Li, Binghui Liu
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引用次数: 0

Abstract

Gaussian graphical models (GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate (FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.

基于图形Lasso多重数据分割的高斯图形模型的可重复学习
高斯图模型作为一种直观、高效的数据分析工具,在许多应用领域得到了广泛的应用。为了解决GGM结构学习的再现性问题,从图模型的角度控制图的估计边缘集的错误发现率(FDR)是至关重要的。因此,近年来,基于FDR控制的GGM估计问题受到越来越多的关注。在本文中,我们提出了一种新的实现多数据分割的GGM估计方法。在多重数据分割中,我们建议直接使用图形Lasso来估计整个精度矩阵,而不是使用逐节点回归来估计精度矩阵的每一行,我们的计算速度比之前的快p倍。结果表明,该方法可以渐近控制FDR,并且在计算效率上具有显著的优势。最后,我们通过实际数据分析证明了所提出方法的有效性。
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来源期刊
CiteScore
1.00
自引率
0.00%
发文量
138
审稿时长
14.5 months
期刊介绍: Acta Mathematica Sinica, established by the Chinese Mathematical Society in 1936, is the first and the best mathematical journal in China. In 1985, Acta Mathematica Sinica is divided into English Series and Chinese Series. The English Series is a monthly journal, publishing significant research papers from all branches of pure and applied mathematics. It provides authoritative reviews of current developments in mathematical research. Contributions are invited from researchers from all over the world.
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