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Outlier robust small area estimation

Record type: Monograph (Working Paper)

Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads down to the idea of an outlier robust bias correction for these estimators. In this paper we develop this idea and also propose two different analytical mean squared error estimators for the ensuring bias corrected outlier robust estimators. Simulations based on realistic outlier contaminated data show that the proposed bias correction often leads to more efficient estimators. Furthermore the proposed mean squared error estimators appear to perform well with a variety of outlier robust smal area estimators.

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Citation

Chambers, Ray, Chandra, Hukum, Salvati, Nicola and Tzavidis, Nikos (2011) Outlier robust small area estimation , Southampton, GB Southampton Statistical Sciences Research Institute, University of Southampton 34pp. (Southampton Statistical Sciences Research Institute Working Paper, M11/07).

More information

Published date: 18 April 2011
Keywords: bias-variance trade-off, linear mixed model, m-estimation, m-quantile model, robust prediction, robust bias correction

Identifiers

Local EPrints ID: 182393
URI: http://eprints.soton.ac.uk/id/eprint/182393
PURE UUID: b970a694-64c0-4cf5-84fd-0a6194229209

Catalogue record

Date deposited: 27 Apr 2011 13:06
Last modified: 18 Jul 2017 11:57

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Contributors

Author: Ray Chambers
Author: Hukum Chandra
Author: Nicola Salvati
Author: Nikos Tzavidis

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