Exact balanced random imputation for sample survey data

TL;DR

This paper introduces an exact balanced random imputation method for survey data that fully eliminates imputation variance, improving the accuracy of estimates in the presence of non-response.

Contribution

It proposes a novel implementation of balanced random imputation that completely removes imputation variance, advancing survey data analysis techniques.

Findings

Imputation variance can be fully eliminated with the proposed method.

The estimator remains consistent under the new imputation approach.

Simulation results support the theoretical findings.

Abstract

Surveys usually suffer from non-response, which decreases the effective sample size. Item non-response is typically handled by means of some form of random imputation if we wish to preserve the distribution of the imputed variable. This leads to an increased variability due to the imputation variance, and several approaches have been proposed for reducing this variability. Balanced imputation consists in selecting residuals at random at the imputation stage, in such a way that the imputation variance of the estimated total is eliminated or at least significantly reduced. In this work, we propose an implementation of balanced random imputation which enables to fully eliminate the imputation variance. Following the approach in Cardot et al. (2013), we consider a regularized imputed estimator of a total and of a distribution function, and we prove that they are consistent under the proposed imputation method. Some simulation results support our findings.