A Dynamic Method for the Evaluation and Comparison of Imputation Techniques

Imputation of missing data is important in many areas, such as reducing non-response bias in surveys and maintaining medical documentation. Estimating the uncertainty inherent in the imputed values is one way of evaluating the results of the imputation process. This paper presents a new method for the estimation of imputation uncertainty, which can be implemented as part of any imputation method, and which can be used to estimate the accuracy of the imputed values generated by both parametric and non-parametric imputation techniques. The proposed approach can be used to assess the feasibility of the imputation process for large complex datasets, and to compare the effectiveness of candidate imputation methods when they are applied to the same dataset. Current uncertainty estimation methods are described and their limitations are discussed. The ideas underpinning the proposed approach are explained in detail, and a case study is presented which shows how the new method has been applied in practice.