Skip to main content

Outlier robust small area estimation

Buy Article:

$51.00 plus tax (Refund Policy)


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

Document Type: Research Article


Publication date: January 1, 2014


Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more