Recursive partitioning on incomplete data using surrogate decisions and multiple imputation
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References listed on IDEAS
- Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
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- Zhang, Mimi & Hu, Qingpei & Xie, Min & Yu, Dan, 2014. "Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 96-111.
- Hapfelmeier, A. & Ulm, K., 2014. "Variable selection by Random Forests using data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 129-139.
- Saiedeh Haji-Maghsoudi & Azam Rastegari & Behshid Garrusi & Mohammad Reza Baneshi, 2018. "Addressing the problem of missing data in decision tree modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 547-557, February.
- Fellinghauer, Bernd & Bühlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.
- Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2015. "Towards a consolidation of worldwide journal rankings – A classification using random forests and aggregate rating via data envelopment analysis," Omega, Elsevier, vol. 51(C), pages 11-23.
More about this item
KeywordsRecursive partitioning; Classification and regression trees; Random Forests; Multiple imputation; MICE; Surrogates;
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