New tools for the imputation of missing values in high-dimensional
data are introduced using the non-parametric nearest neighbor methods. It
includes weighted nearest neighbor imputation methods that use specific
distances for selected variables. It includes an automatic procedure of cross
validation and does not require prespecified values of the tuning parameters.
It can be used to impute missing values in high-dimensional data when the sample
size is smaller than the number of predictors. For more information see Faisal
and Tutz (2017)