Balanced k-nearest neighbour imputation

ABSTRACT

Random imputation is an interesting class of imputation methods to handle item nonresponse because it tends to preserve the distribution of the imputed variable. However, such methods amplify the total variance of the estimators because values are imputed at random. This increase in variance is called imputation variance. In this paper, we propose a new random hot-deck imputation method that is based on the k-nearest neighbour methodology. It replaces the missing value of a unit with the observed value of a similar unit. Calibration and balanced sampling are applied to minimize the imputation variance. Moreover, our proposed method provides triple protection against nonresponse bias. This means that if at least one out of three specified models holds, then the resulting total estimator is unbiased. Finally, our approach allows the user to perform consistency edits and to impute simultaneously.

KEYWORDS:

• Balanced sampling
• calibration
• missing data
• nonresponse

CLASSIFICATION:

• 62D05

Acknowledgments

We thank the two reviewers, associate editor, and editor for their constructive comments and suggestions. Those helped us to considerably improve this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the Swiss National Science Foundation, project number P1NEP2_151904 and the Swiss Federal Statistical Office.