Imputation using higher order moments of an auxiliary variable

In this article, we propose a new method of imputation that makes use of higher order moments of an auxiliary variable while imputing missing values. The mean, ratio, and regression methods of imputation are shown to be special cases and less efficient than the newly developed method of imputation, which makes use of higher order moments. Analytical comparisons show that the first-order mean squared error approximation for the proposed new method of imputation is always smaller than that for the regression method of imputation. At the end, the proposed higher order moments-based imputation method has been applied to a real dataset.