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Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis

Author

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  • Yongsong Qin

    (Guangxi Normal University, China)

  • Shichao Zhang

    (Zhejiang Normal University, China and University of Technology, Australia)

  • Chengqi Zhang

    (University of Technology, Australia)

Abstract

The k-nearest neighbor (kNN) 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 kNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the kNN 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 kNN imputation method.

Suggested Citation

  • Yongsong Qin & Shichao Zhang & Chengqi Zhang, 2010. "Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 6(4), pages 61-73, October.
    • Handle: RePEc:igg:jdwm00:v:6:y:2010:i:4:p:61-73
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