Outlier robust small area estimation
Outlier robust small area estimation
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.
bias-variance trade-off, linear mixed model, m-estimation, m-quantile model, robust prediction, robust bias correction
Southampton Statistical Sciences Research Institute, University of Southampton
Chambers, Ray
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Chandra, Hukum
347edcad-4980-446f-8fc8-f4e1e5e3e79e
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
18 April 2011
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Chandra, Hukum
347edcad-4980-446f-8fc8-f4e1e5e3e79e
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Chambers, Ray, Chandra, Hukum, Salvati, Nicola and Tzavidis, Nikos
(2011)
Outlier robust small area estimation
(Southampton Statistical Sciences Research Institute Working Paper, M11/07)
Southampton, GB.
Southampton Statistical Sciences Research Institute, University of Southampton
34pp.
Record type:
Monograph
(Working Paper)
Abstract
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.
Text
s3ri-workingpaper-M11-07.pdf
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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
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Date deposited: 27 Apr 2011 13:06
Last modified: 15 Mar 2024 03:11
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Contributors
Author:
Ray Chambers
Author:
Hukum Chandra
Author:
Nicola Salvati
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