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Kernel classification with missing data and the choice of smoothing parameters

Author

Listed:
  • Levon Demirdjian

    (University of California)

  • Majid Mojirsheibani

    (California State University)

Abstract

Methods are proposed for selecting smoothing parameters of kernel classifiers in the presence of missing covariates. Here the missing covariates can appear in both the data and in the unclassified observation that has to be classified. The proposed methods are quite straightforward to implement. Exponential performance bounds will be derived for the resulting classifiers. Such bounds, in conjunction with the Borel–Cantelli lemma, provide various strong consistency results. Several numerical examples are presented to illustrate the effectiveness of the proposed procedures.

Suggested Citation

  • Levon Demirdjian & Majid Mojirsheibani, 2019. "Kernel classification with missing data and the choice of smoothing parameters," Statistical Papers, Springer, vol. 60(5), pages 1487-1513, October.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:5:d:10.1007_s00362-017-0883-y
    DOI: 10.1007/s00362-017-0883-y
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    References listed on IDEAS

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