Robust Gaussian graphical modeling
Robust Gaussian graphical modeling
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DOI：
10.1016/j.jmva.2006.02.006
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发表时间：
2006-08-01
影响因子：
1.6
通讯作者：
Kano, Y
中科院分区：
文献类型：
--
作者：
Miyamura, M;Kano, Y
提出了一种新的高斯图模型，对可能的离群值进行了鲁棒化。似然函数根据观测值的偏差进行加权，其中观测值的偏差基于其似然来测量。与鲁棒估计相关的检验统计量的开发。这些包括模型拟合优度的统计。一个离群分数，类似于，但更强大的比马氏距离，也提出了。新的分数使识别外围观测更容易。Monte Carlo模拟和对一个真实的数据集的分析表明，所提出的方法比普通的高斯图模型和其他一些稳健的多变量估计更好。(c)2006年爱思唯尔公司All rights reserved.
A new Gaussian graphical modeling that is robustified against possible outliers is proposed. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its likelihood. Test statistics associated with the robustified estimators are developed. These include statistics for goodness of fit of a model. An outlying score, similar to but more robust than the Mahalanobis distance, is also proposed. The new scores make it easier to identify outlying observations. A Monte Carlo simulation and an analysis of a real data set show that the proposed method works better than ordinary Gaussian graphical modeling and some other robustified multivariate estimators. (c) 2006 Elsevier Inc. All rights reserved.