Abstract
In the framework of incomplete data analysis, this paper provides a nonparametric approach to missing data imputation based on Information Retrieval. In particular, an incremental procedure based on the iterative use of tree-based method is proposed and a suitable Incremental Imputation Algorithm is introduced. The key idea is to define a lexicographic ordering of cases and variables so that conditional mean imputation via binary trees can be performed incrementally. A simulation study and real data applications are carried out to describe the advantages and the performance with respect to standard approaches.
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The authors would like to thank the editor and referees for their helpful comments, which have helped us to greatly improve the quality of this paper. Research work of Claudio Conversano is supported by the research funds awarded by University of Cagliari within the “Young Researchers’ Start-Up Programme 2007”. Research work of Roberta Siciliano is supported by the “iWebCare” European Project research funds (FP6-2004-IST-4-028055).
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Conversano, C., Siciliano, R. Incremental Tree-Based Missing Data Imputation with Lexicographic Ordering. J Classif 26, 361–379 (2009). https://doi.org/10.1007/s00357-009-9038-8
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DOI: https://doi.org/10.1007/s00357-009-9038-8