Regression-based nearest neighbour hot decking

SummaryThe paper develops the imputation method which takes advantage both of a multivariate regression model and a nearest neighbour hot decking method. This method is successfully applied to such complex cases where the variable being imputed is of a ratio-scale type and consists of a high number of unknown zero values. The results obtained by means of the method are compared with the two other techniques, (i) random hot decking and (ii) two-step model based method. The latter one first takes advantage of logistic regression and then of standard regression imputation. Our results do not give the only one conclusion. On average, regression based nearest neighbour hot decking is the best, but the two-step model based method also has some advantages. The paper cannot deal with other important questions, but we want to emphasise the importance of variance estimation: it leads to an additional variance component called imputation variance. The paper also discusses a diagnostic test for the quality of imputations; this test checks how many times the same donor is used in imputing missing values.