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Recursive partitioning for missing data imputation in the presence of interaction effects

Author

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  • Doove, L.L.
  • Van Buuren, S.
  • Dusseldorp, E.

Abstract

Standard approaches to implement multiple imputation do not automatically incorporate nonlinear relations like interaction effects. This leads to biased parameter estimates when interactions are present in a dataset. With the aim of providing an imputation method which preserves interactions in the data automatically, the use of recursive partitioning as imputation method is examined. Three recursive partitioning techniques are implemented in the multiple imputation by chained equations framework. It is investigated, using simulated data, whether recursive partitioning creates appropriate variability between imputations and unbiased parameter estimates with appropriate confidence intervals. It is concluded that, when interaction effects are present in a dataset, substantial gains are possible by using recursive partitioning for imputation compared to standard applications. In addition, it is shown that the potential of recursive partitioning imputation approaches depends on the relevance of a possible interaction effect, the correlation structure of the data, and the type of possible interaction effect present in the data.

Suggested Citation

  • Doove, L.L. & Van Buuren, S. & Dusseldorp, E., 2014. "Recursive partitioning for missing data imputation in the presence of interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 92-104.
    • Handle: RePEc:eee:csdana:v:72:y:2014:i:c:p:92-104
    DOI: 10.1016/j.csda.2013.10.025
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    References listed on IDEAS

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    1. Iacus, Stefano M. & Porro, Giuseppe, 2007. "Missing data imputation, matching and other applications of random recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 773-789, October.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    4. Iacus, Stefano & Porro, Giuseppe, 2008. "Invariant and Metric Free Proximities for Data Matching: An R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i11).
    5. Nonyane Bareng A. S. & Foulkes Andrea S., 2007. "Multiple Imputation and Random Forests (MIRF) for Unobservable, High-Dimensional Data," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-19, August.
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