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Recursive partitioning on incomplete data using surrogate decisions and multiple imputation


  • Hapfelmeier, A.
  • Hothorn, T.
  • Ulm, K.


The occurrence of missing data is a major problem in statistical data analysis. All scientific fields and data of all kinds and size are touched by this problem. There is a number of ad-hoc solutions which unfortunately lead to a loss of power, biased inference, underestimation of variability and distorted relationships between variables. A more promising approach of rising popularity is multiple imputation by chained equations (MICE) also known as imputation by full conditional specification (FCS). Alternatives to imputation are given by methods with built-in procedures. These include recursive partitioning by classification and regression trees as well as corresponding Random Forests. However there is only few literature comparing the two approaches. Existing evaluations often lack generalizability due to restrictions on data structure and simulation schemes. The application of both methods to several kinds of data and different simulation settings is meant to improve and extend the comparative analyses. Classification and regression studies are examined. Recursive partitioning is executed by two popular tree and one Random Forest implementation. Findings show that multiple imputation produces ambiguous performance results for both, simulated and real life data. Using surrogates instead is a fast and simple way to achieve performances which are only negligible worse and in many cases even superior.

Suggested Citation

  • Hapfelmeier, A. & Hothorn, T. & Ulm, K., 2012. "Recursive partitioning on incomplete data using surrogate decisions and multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1552-1565.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1552-1565
    DOI: 10.1016/j.csda.2011.09.024

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    References listed on IDEAS

    1. 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.
    2. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
    3. Templ, Matthias & Kowarik, Alexander & Filzmoser, Peter, 2011. "Iterative stepwise regression imputation using standard and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2793-2806, October.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.


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