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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 Learningtechniques is tempting. Here, the recently proposed missForest imputationmethod has shown high imputation accuracy under the Missing (Completely) atRandom scheme with various missing rates. In its core, it is based on a randomforest for classification and regression, respectively. In this paper we studywhether this approach can even be enhanced by other methods such as thestochastic gradient tree boosting method, the C5.0 algorithm or modified randomforest procedures. In particular, other resampling strategies within the randomforest protocol are suggested. In an extensive simulation study, we analyzetheir performances for continuous, categorical as well as mixed-type data.Therein, MissBooPF, a combination of the stochastic gradient tree boostingmethod together with the parametrically bootstrapped random forest method,appeared to be promising. Finally, an empirical analysis focusing on creditinformation and Facebook data is conducted.

Who wins the Miss Contest for Imputation Methods? Our Vote for Miss BooPF