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 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.
bias–variance trade-off, linear mixed model, m-estimation, m-quantile model, robust bias correction, robust prediction
1-23
Chambers, R.L.
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Chandra, H.
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Salvati, N.
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Tzavidis, N.
431ec55d-c147-466d-9c65-0f377b0c1f6a
Chambers, R.L.
df4b494f-3260-4198-8137-3bf1d9c60fa2
Chandra, H.
74cd7dcc-d320-4ba2-8cf2-02cd5aa3216c
Salvati, N.
d1b7ebe3-afad-40fb-b32c-e748e344e922
Tzavidis, N.
431ec55d-c147-466d-9c65-0f377b0c1f6a
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, .
(doi:10.1111/rssb.12019).
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 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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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
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Date deposited: 27 Apr 2011 14:39
Last modified: 15 Mar 2024 03:11
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Contributors
Author:
R.L. Chambers
Author:
H. Chandra
Author:
N. Salvati
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