Boosted Incremental Tree-based Imputation of Missing Data

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 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 proposed method allows more accurate imputations 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.