Improving robust ratio estimation in longitudinal surveys with outlier observations

Abstract

The Hulliger’s robust estimation technique consists in the re-weighting of units identified as outliers through a Robustified Ratio Estimator (RRE), according to which outliers contribute to the final estimate with a sample weight reduced with respect to the original one. Outlier observations are identified through a standardised function founded on the difference between observed and expected values. A crucial aspect concerns the choice of the acceptation threshold, which plays a role in the re-weighting process as well. In this context, we propose some potential improvements of the RRE, concerning the use of an objective criterion for fixing the threshold and the re-weighting rules. Results of two empirical attempts based on real data derived from longitudinal surveys show that, in the most part of case studies, the proposed changes contribute to improve efficiency of estimates with respect to the ordinary ratio estimator