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).


Full text not available from this repository.


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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1111/rssb.12019
ISSNs: 1369-7412 (print)
1467-9868 (electronic)
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
ePrint ID: 181955
Accepted Date and Publication Date:
20 March 2013Made publicly available
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
URI: http://eprints.soton.ac.uk/id/eprint/181955

Actions (login required)

View Item View Item