We develop a nonparametric imputation method for item nonresponse based on the well-known hot deck approach. The proposed imputation method is developed for imputing numerical data that ensure that all record-level edit rules are satisfied and previously estimated or known totals are exactly preserved. We propose a sequential hot deck imputation approach that takes into account survey weights. Original survey weights are not changed; rather the imputations themselves are calibrated so that weighted estimates will equal known or estimated population totals. Edit rules are preserved by integrating the sequential hot deck imputation with Fourier-Motzkin elimination, which defines the range of feasible values that can be used for imputation such that all record-level edits will be satisfied. We apply the proposed imputation method under different scenarios of random and nearest-neighbor hot deck on two data sets: an annual structural business survey and a synthetically generated data set with a large proportion of missing data. We compare the proposed imputation methods to standard imputation methods based on a set of evaluation measures.
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