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 an incremental algorithm based on cross-validated decision trees and a lexicographic ordering of the single data to be imputed. This paper considers ail 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 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.