Improved Methods for the Imputation of Missing Data by Nearest Neighbor Methods

Missing data is an important issue in almost all fields of
quantitative research. A nonparametric procedure that has been
shown to be useful is the nearest neighbor imputation method. We
suggest a weighted nearest neighbor imputation method based on
Lq-distances. The weighted method is shown to have smaller
imputation error than available NN estimates. In addition we
consider weighted neighbor imputation methods that use selected
distances. The careful selection of distances that carry
information on the missing values yields an imputation tool that
outperforms competing nearest neighbor methods distinctly.
Simulation studies show that the suggested weighted imputation with
selection of distances provides the smallest imputation error, in
particular when the number of predictors is large. In addition, the
selected procedure is applied to real data from different fields.