Combining κNN imputation and bootstrap calibrated empirical likelihood for incomplete data analysis

Publication Type:
Journal Article
Citation:
International Journal of Data Warehousing and Mining, 2010, 6 (4), pp. 61 - 73
Issue Date:
2010-10-01
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The κ-nearest neighbor (κNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing (or incomplete) data that is a combination of the κNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the κNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with κNN imputation method. Copyright © 2010.
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