Improving robust ratio estimation in longitudinal surveys with outlier observations

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 id...

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Main Author: Roberto Gismondi
Format: Article
Language:English
Published: University of Bologna 2013-05-01
Series:Statistica
Online Access:http://rivista-statistica.unibo.it/article/view/3575
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author Roberto Gismondi
author_facet Roberto Gismondi
author_sort Roberto Gismondi
collection DOAJ
description 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.
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spelling doaj.art-a817deaa471d4794bc56a7f675c3248f2022-12-22T00:14:58ZengUniversity of BolognaStatistica0390-590X1973-22012013-05-01701233910.6092/issn.1973-2201/35753321Improving robust ratio estimation in longitudinal surveys with outlier observationsRoberto Gismondi0ISTAT, Italian National Statistical InstituteThe 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.http://rivista-statistica.unibo.it/article/view/3575
spellingShingle Roberto Gismondi
Improving robust ratio estimation in longitudinal surveys with outlier observations
Statistica
title Improving robust ratio estimation in longitudinal surveys with outlier observations
title_full Improving robust ratio estimation in longitudinal surveys with outlier observations
title_fullStr Improving robust ratio estimation in longitudinal surveys with outlier observations
title_full_unstemmed Improving robust ratio estimation in longitudinal surveys with outlier observations
title_short Improving robust ratio estimation in longitudinal surveys with outlier observations
title_sort improving robust ratio estimation in longitudinal surveys with outlier observations
url http://rivista-statistica.unibo.it/article/view/3575
work_keys_str_mv AT robertogismondi improvingrobustratioestimationinlongitudinalsurveyswithoutlierobservations