Bootstrap and Nonparametric Predictors to Impute Missing Data

A new nonparametric technique to impute missing data is proposed in order to obtain a completed data-matrix, capable of producing a degree of reliability for the imputations. Without taking into account strong assumptions, we introduce multiple imputations using bootstrap and nonparametric predictors. It is shown that, in this manner, we can obtain better imputations than with other known methods producing a more reliable completed data-matrix. Using two simulations, we show that the proposed technique can be generalized to consider non-monotone patterns of missing data with interesting results.