Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables
Bernd Fellinghauer,
Peter Bühlmann,
Martin Ryffel,
Michael von Rhein and
Jan D. Reinhardt
Computational Statistics & Data Analysis, 2013, vol. 64, issue C, 132-152
Abstract:
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.
Keywords: Graphical model; High dimensions; LASSO; Mixed data; Random Forests; Stability Selection (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (10)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:64:y:2013:i:c:p:132-152
DOI: 10.1016/j.csda.2013.02.022
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