Incremental Tree-Based Missing Data Imputation with Lexicographic Ordering

被引:18
|
作者
Conversano, Claudio [1 ]
Siciliano, Roberta [2 ]
机构
[1] Univ Cagliari, Dept Econ, I-09123 Cagliari, Italy
[2] Univ Naples Federico 2, I-80126 Naples, Italy
关键词
Missing data; Classification and regression tree; FAST splitting algorithm; Lexicographic order; Nonparametric imputation; Data editing; REGRESSION;
D O I
10.1007/s00357-009-9038-8
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页码:361 / 379
页数:19
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