Title: Nearest neighbor imputation for general parameter estimation in survey sampling,
Abstract: Nearest neighbor imputation is popular 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,
the conventional bootstrap inference for matching estimators with fixed number
of matches has been shown to be invalid due to the nonsmoothness nature of the
matching estimator. We propose asymptotically valid replication variance
estimation. The key strategy is to construct replicates of the estimator
directly based on linear terms, instead of individual records of variables. A
simulation study confirms that the new procedure provides valid variance
estimation.