Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables
Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables
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DOI：
10.1016/j.csda.2013.02.022
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发表时间：
2013-08-01
影响因子：
1.8
通讯作者：
Reinhardt, Jan D.
中科院分区：
文献类型：
--
作者：
Fellinghauer, Bernd;Buehlmann, Peter;Reinhardt, Jan D.
随机森林与稳定性选择相结合，允许估计稳定的条件独立图，并具有错误控制机制，用于误报选择。这种方法适用于同时包含连续变量和离散变量的图。它的性能进行评估，在各种模拟设置和替代方法进行比较。最后，该方法被应用到两个健康相关的数据集，第一个研究功能健康组件，个人和环境因素的相互联系，第二个确定风险因素，这可能与心脏直视手术后不良神经发育。(c)2013爱思唯尔有限公司版权所有。
Random Forests in combination with Stability Selection allow to estimate stable conditional independence graphs with an error control mechanism for false positive selection. This approach is applicable to graphs containing both continuous and discrete variables at the same time. Its performance is evaluated in various simulation settings and compared with alternative approaches. Finally, the approach is applied to two heath-related data sets, first to study the interconnection of functional health components, personal, and environmental factors and second to identify risk factors which may be associated with adverse neurodevelopment after open-heart surgery. (c) 2013 Elsevier B.V. All rights reserved.