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Improved methods for the imputation of missing data by nearest neighbor methods

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  • Tutz, Gerhard
  • Ramzan, Shahla

Abstract

Missing data raise problems in almost all fields of quantitative research. A useful nonparametric procedure is the nearest neighbor imputation method. Improved versions of this method are presented. First, a weighted nearest neighbor imputation method based on Lq distances is proposed. It is demonstrated that the method tends to have a smaller imputation error than other nearest neighbor estimates. Then weighted nearest neighbor imputation methods that use distances for selected covariates are considered. The careful selection of distances that carry information about the missing values yields an imputation tool that can outperform competing nearest neighbor methods. This approach performs well, especially when the number of predictors is large. The methods are evaluated in simulation studies and with several real data sets from different fields.

Suggested Citation

  • Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
    • Handle: RePEc:eee:csdana:v:90:y:2015:i:c:p:84-99
    DOI: 10.1016/j.csda.2015.04.009
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    References listed on IDEAS

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    1. Feten Guri & Almøy Trygve & Aastveit Are H., 2005. "Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-18, May.
    2. Hron, K. & Templ, M. & Filzmoser, P., 2010. "Imputation of missing values for compositional data using classical and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3095-3107, December.
    3. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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    Cited by:

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    4. Faisal Shahla & Tutz Gerhard, 2017. "Missing value imputation for gene expression data by tailored nearest neighbors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 95-106, April.

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