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 t...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2014-01, Vol.76 (1), p.47-69
Main Authors: Chambers, Ray, Chandra, Hukum, Salvati, Nicola, Tzavidis, Nikos
Format: Article
Language:English
Subjects:
Area studies
Bias
Bias-variance trade-off
data analysis
Distribution
Error
Error analysis
Errors
Estimating techniques
Estimators
Linear mixed model
M-estimation
M-quantile model
Mathematical analysis
Methodology
probability distribution
Rest
Robust bias correction
Robust prediction
Simulation
Statistical analysis
Statistics
Studies
Survey data
surveys
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Description
Summary: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.
ISSN:1369-7412
1467-9868
DOI:10.1111/rssb.12019