Abstract
Tree-based procedures have been recently considered as non parametric tools for missing data imputation when dealing with large data structures and no probability assumption. A previous work used an incremental algorithm based on cross-validated decision trees and a lexicographic ordering of the single data to be imputed. This paper considers an ensemble method where tree-based model is used as learner. Furthermore, the incremental imputation concerns missing data of each variable at turn. As a result, the proposed method allows more accurate imputa-tions through a more efficient algorithm. A simulation case study shows the overall good performance of the proposed method against some competitors. A MatLab implementation enriches Tree Harvest Software for non-standard classification and regression trees.
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Siciliano, R., Aria, M., D’Ambrosio, A. (2006). Boosted Incremental Tree-based Imputation of Missing Data. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_31
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DOI: https://doi.org/10.1007/3-540-35978-8_31
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