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.
Dr. Yang is partially supported by NSF grant DMS 1811245, NCI grant P01 CA142538, and Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities. Dr. Kim is partially supported by NSF grant MMS 1733572.
Yang, S. and Kim, J.K. (2019), "Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling", The Econometrics of Complex Survey Data (Advances in Econometrics, Vol. 39), Emerald Publishing Limited, Bingley, pp. 209-234. https://doi.org/10.1108/S0731-905320190000039012
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