Outlier robust small area estimation
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, 1-23. (doi:10.1111/rssb.12019).
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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.
|Digital Object Identifier (DOI):||doi:10.1111/rssb.12019|
|Keywords:||bias–variance trade-off, linear mixed model, m-estimation, m-quantile model, robust bias correction, robust prediction|
|Subjects:||H Social Sciences > HA Statistics|
|Divisions:||University Structure - Pre August 2011 > School of Social Sciences > Social Statistics
|Date Deposited:||27 Apr 2011 14:39|
|Last Modified:||31 Mar 2016 13:36|
Small area methods for poverty and living condition estimates (SAMPLE)
Funded by: European Commission - FP7 (217565)
1 March 2008 to 28 February 2011
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