Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
复制标题
学习具有不同节点数的线性非高斯有向无环图
DOI：
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发表时间：
2021-11
影响因子：
6
通讯作者：
Wang Junhui
中科院分区：
文献类型：
--
作者：
Zhao Ruixuan;HE Xin;Wang Junhui
无环模型通常被描述为有向无环图(DAG)，被广泛地用来表示收集节点之间的方向因果关系。在本文中，我们提出了一种在高维情况下学习线性非高斯DAG的有效方法，其中噪声可以是任何连续的非高斯分布。这与大多数现有的DAG学习方法形成鲜明对比，该方法假设高斯噪声和附加的方差假设，以实现准确的DAG恢复。该方法利用拓扑层的新概念来促进DAG的学习。特别是，我们证明了拓扑层可以以自底向上的方式精确地重建，并且每个层中的节点之间的父子关系也可以一致地建立。更重要的是，所提出的方法不需要在DAG学习文献中广泛假设的忠诚度或父母忠诚度假设。在各种模拟例子中与一些热门竞争对手的数值比较，以及在新冠肺炎全球传播上的实际应用，也支持了它的优势。
Acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. This is in sharp contrast to most existing DAG learning methods assuming Gaussian noise with additional variance assumptions to attain exact DAG recovery. The proposed method leverages a novel concept of topological layer to facilitate the DAG learning. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes in each layer can also be consistently established. More importantly, the proposed method does not require the faithfulness or parental faithfulness assumption which has been widely assumed in the literature of DAG learning. Its advantage is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19.
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影响因子：
4.5
作者：
Uhler, Caroline;Raskutti, Garvesh;Yu, Bin
通讯作者：
Yu, Bin
影响因子：
2.7
作者：
Yiping Yuan;Xiaotong Shen;W. Pan;Zizhuo Wang
通讯作者：
Yiping Yuan;Xiaotong Shen;W. Pan;Zizhuo Wang
DOI：
10.1214/09-aoas312
发表时间：
2009-01-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Kosorok MR
通讯作者：
Kosorok MR
DOI：
10.5555/2567709.2502585
发表时间：
2013-01
期刊：
Journal of machine learning research : JMLR
影响因子：
--
作者：
Aapo Hyvärinen;Stephen M. Smith
通讯作者：
Aapo Hyvärinen;Stephen M. Smith
DOI：
--
发表时间：
2017-11
期刊：
--
影响因子：
--
作者：
Jonas Peters;D. Janzing;Bernhard Schölkopf
通讯作者：
Jonas Peters;D. Janzing;Bernhard Schölkopf