Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables

被引：50

作者：

Fellinghauer, Bernd ^{[1
,2
]}

Buehlmann, Peter ^{[2
]}

Ryffel, Martin ^{[2
]}

von Rhein, Michael ^{[3
]}

Reinhardt, Jan D. ^{[1
,4
]}

机构：

[1] Swiss Parapleg Res, Nottwil, Switzerland

[2] ETH, Seminar Stat, CH-8092 Zurich, Switzerland

[3] Univ Childrens Hosp, Child Dev Ctr, Zurich, Switzerland

[4] Univ Lucerne, Dept Hlth Sci & Hlth Policy, Luzern, Switzerland

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2013年 / 64卷

关键词：

Graphical model; High dimensions; LASSO; Mixed data; Random Forests; Stability Selection;

D O I：

10.1016/j.csda.2013.02.022

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：132 / 152

页数：21

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