Outlier robust small area estimation

Ray Chambers

doi:10.1111/rssb.12019

Outlier robust small area estimation

2013-03-20

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AI

DOI：10.1111/rssb.12019

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Abstract

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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.

Keywords:

Bias-variance trade-off

Linear mixed model

M-estimation

M-quantile model

Robust bias correction

Robust prediction

Outlier robust small area estimation

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