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Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling

In: The Econometrics of Complex Survey Data

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  • Shu Yang
  • Jae Kwang Kim

Abstract

Nearest neighbor imputation has a long tradition for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the nearest neighbor imputation estimator for general population parameters, including population means, proportions and quantiles. For variance estimation, we propose novel replication variance estimation, which is asymptotically valid and straightforward to implement. The main idea is to construct replicates of the estimator directly based on its asymptotically linear terms, instead of individual records of variables. The simulation results show that nearest neighbor imputation and the proposed variance estimation provide valid inferences for general population parameters.

Suggested Citation

  • Shu Yang & Jae Kwang Kim, 2019. "Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling," Advances in Econometrics, in: The Econometrics of Complex Survey Data, volume 39, pages 209-234, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320190000039012
    DOI: 10.1108/S0731-905320190000039012
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    Citations

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    Cited by:

    1. Shu Yang & Jae Kwang Kim, 2020. "Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 839-861, September.
    2. Chenyin Gao & Katherine Jenny Thompson & Jae Kwang Kim & Shu Yang, 2022. "Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1903-1930, October.
    3. Maciej Berk{e}sewicz & Greta Bia{l}kowska & Krzysztof Marcinkowski & Magdalena Ma'slak & Piotr Opiela & Robert Pater & Katarzyna Zadroga, 2019. "Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration," Papers 1908.06731, arXiv.org.

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