Network granger causality with inherent grouping structure
Network granger causality with inherent grouping structure
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DOI：
10.5555/2789272.2789285
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Sumanta Basu;A. Shojaie;G. Michailidis
中科院分区：
文献类型：
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作者：
Sumanta Basu;A. Shojaie;G. Michailidis
在许多生物和社会经济系统的分析中，很自然地出现了估计高维网络模型的问题。在这项工作中，我们的目标是从时间面板数据中学习网络结构，采用Granger因果模型的框架，假设网络结构的边是稀疏的，节点之间存在内在的分组结构。为此，我们引入了一个组套索回归正则化框架，并研究了一个阈值变量来解决组误指定的问题。在此基础上，建立了估计的范数相合性和变量选择相合性，后者是在新的方向相合性概念下得到的。通过一系列广泛的模拟研究和与现有技术的比较来评估所提出的方法的性能。这项研究以来自功能基因组学和金融计量经济学的两个振奋人心的例子为例。
The problem of estimating high-dimensional network models arises naturally in the analysis of many biological and socio-economic systems. In this work, we aim to learn a network structure from temporal panel data, employing the framework of Granger causal models under the assumptions of sparsity of its edges and inherent grouping structure among its nodes. To that end, we introduce a group lasso regression regularization framework, and also examine a thresholded variant to address the issue of group misspecification. Further, the norm consistency and variable selection consistency of the estimates are established, the latter under the novel concept of direction consistency. The performance of the proposed methodology is assessed through an extensive set of simulation studies and comparisons with existing techniques. The study is illustrated on two motivating examples coming from functional genomics and financial econometrics.