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Missing values are a common phenomenon in all areas of applied research.While various imputation methods are available for metrically scaled variables,methods for categorical data are scarce. An imputation method that has beenshown to work well for high dimensional metrically scaled variables is theimputation by nearest neighbor methods. In this paper, we extend the weightednearest neighbors approach to impute missing values in categorical variables.The proposed method, called $\mathtt{wNNSel_{cat}}$, explicitly uses theinformation on association among attributes. The performance of differentimputation methods is compared in terms of the proportion of falsely imputedvalues. Simulation results show that the weighting of attributes yields smallerimputation errors than existing approaches. A variety of real data sets is usedto support the results obtained by simulations.

Nearest Neighbor Imputation for Categorical Data by Weighting of Attributes