Estimation of Symmetry-Constrained Gaussian Graphical Models: Application to Clustered Dense Networks
Estimation of Symmetry-Constrained Gaussian Graphical Models: Application to Clustered Dense Networks
复制标题
对称约束高斯图模型的估计：在集群密集网络中的应用
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
H. Massam
中科院分区：
文献类型：
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作者：
Xin Gao;H. Massam
提出了一种针对高维聚类数据的模型选择算法。该算法将经典的惩罚似然方法与彩色图形高斯模型框架下的复合似然方法相结合。我们的方法旨在识别具有大量边缘但边缘类稀疏的高维密集网络。通过模拟研究和基因表达数据集的网络分析证明了其经验性能。
We propose a model selection algorithm for high-dimensional clustered data. Our algorithm combines a classical penalized likelihood method with a composite likelihood approach in the framework of colored graphical Gaussian models. Our method is designed to identify high-dimensional dense networks with a large number of edges but sparse edge classes. Its empirical performance is demonstrated through simulation studies and a network analysis of a gene expression dataset.