Stable graphical model estimation with random forests for discrete, continuous, and mixed variables

DOI10.1016/j.csda.2013.02.022zbMath1468.62053arXiv1109.0152OpenAlexW2165928777MaRDI QIDQ1800084

Bernd Fellinghauer, Martin Ryffel, Michael von Rhein, Jan D. Reinhardt, Peter Bühlmann

Publication date: 19 October 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1109.0152

zbMATH Keywords

random forestsmixed datagraphical modelLassohigh dimensionsstability selection

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic graphical models (62H22)

Related Items (14)

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Uses Software

• R
• Stata
• glasso
• Rmpi
• glmnet
• rpartOrdinal
• missForest
• confa

Cites Work

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• Bootstrap methods: another look at the jackknife
• Subsampling
• Analyzing bagging
• A new variable selection approach using random forests
• High-dimensional graphs and variable selection with the Lasso
• Decomposition and Model Selection for Large Contingency Tables
• The Group Lasso for Logistic Regression
• Stability Selection
• Random forests

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