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Predicting missing values: a comparative study on non-parametric approaches for imputation

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  • Burim Ramosaj

    (Technical University of Dortmund)

  • Markus Pauly

    (Technical University of Dortmund)

Abstract

Missing data is an expected issue when large amounts of data is collected, and several imputation techniques have been proposed to tackle this problem. Beneath classical approaches such as MICE, the application of Machine Learning techniques is tempting. Here, the recently proposed missForest imputation method has shown high imputation accuracy under the Missing (Completely) at Random scheme with various missing rates. In its core, it is based on a random forest for classification and regression, respectively. In this paper we study whether this approach can even be enhanced by other methods such as the stochastic gradient tree boosting method, the C5.0 algorithm, BART or modified random forest procedures. In particular, other resampling strategies within the random forest protocol are suggested. In an extensive simulation study, we analyze their performances for continuous, categorical as well as mixed-type data. An empirical analysis focusing on credit information and Facebook data complements our investigations.

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  • Burim Ramosaj & Markus Pauly, 2019. "Predicting missing values: a comparative study on non-parametric approaches for imputation," Computational Statistics, Springer, vol. 34(4), pages 1741-1764, December.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:4:d:10.1007_s00180-019-00900-3
    DOI: 10.1007/s00180-019-00900-3
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    References listed on IDEAS

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