Variance estimation in the presence of imputed data for high entropy sampling designs

Variance estimation is an important aspect of the estimation process in statistical agencies as variance estimates provide a measure of the quality of the survey’s estimates. In the presence of imputed data, usual variance estimators rely on the availability of the second-order inclusion
probabilities, which may be difficult (even impossible) to compute for ome sampling designs. In this paper, we derive simplified variance estimators that result from approximating the second-order inclusion probabilities in terms of the first-order inclusion probabilities. Results
of a simulation study, evaluating the properties of the proposed variance estimators in terms of bias and mean squared error, will be presented.