Association-Based Multiple Imputation in Multivariate Datasets: A Summary
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Incorporating an EM-Approach for Handling Missing Attribute-Values in Decision Tree Induction
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Semi-parametric optimization for missing data imputation
Applied Intelligence
Missing value imputation based on data clustering
Transactions on computational science I
A Novel Framework for Imputation of Missing Values in Databases
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Many missing data analysis techniques are of single-imputation. However, single-imputation cannot provide valid standard errors and confidence intervals, since it ignores the uncertainty implicit in the fact that the imputed values are not the actual values. Filling in each missing value with a set of plausible values is called multi-imputation. In this paper we propose a kernel-based stochastic non-parametric multi-imputation method under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. Furthermore, we present a kernel-based stochastic semi-parametric multi-imputation method while we have some priori knowledge about the dataset with missing. Our algorithms are designed specifically with the aim of optimizing the confidence-interval and the relative efficiency. The proposed technique is evaluated by experimentations, using simulation data and real data, and the results demonstrate that our method performs much better than the NORM method, and is promising.