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Outlier robust small area estimation

Record type: Article

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 one to consider an outlier robust bias correction for these estimators. We develop this idea, proposing two different analytical mean-squared error estimators for the ensuing bias-corrected outlier robust estimators. Simulations based on realistic outlier-contaminated data show that the bias correction proposed often leads to more efficient estimators. Furthermore, the mean-squared error estimation methods proposed appear to perform well with a variety of outlier robust small area estimators.

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Citation

Chambers, R.L., Chandra, H., Salvati, N. and Tzavidis, N. (2013) Outlier robust small area estimation Journal of the Royal Statistical Society: Series B (Statistical Methodology), n/a, pp. 1-23. (doi:10.1111/rssb.12019).

More information

e-pub ahead of print date: 20 March 2013
Keywords: bias–variance trade-off, linear mixed model, m-estimation, m-quantile model, robust bias correction, robust prediction
Organisations: Social Statistics

Identifiers

Local EPrints ID: 181955
URI: http://eprints.soton.ac.uk/id/eprint/181955
ISSN: 1467-9868
PURE UUID: 3dddaece-6eac-4fbc-9695-6d71bd5de035

Catalogue record

Date deposited: 27 Apr 2011 14:39
Last modified: 18 Jul 2017 11:58

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Contributors

Author: R.L. Chambers
Author: H. Chandra
Author: N. Salvati
Author: N. Tzavidis

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