Economics at your fingertips  

Small area estimation when auxiliary information is measured with error

Lynn M. R. Ybarra and Sharon L. Lohr

Biometrika, 2008, vol. 95, issue 4, 919-931

Abstract: Small area estimation methods typically combine direct estimates from a survey with predictions from a model in order to obtain estimates of population quantities with reduced mean squared error. When the auxiliary information used in the model is measured with error, using a small area estimator such as the Fay--Herriot estimator while ignoring measurement error may be worse than simply using the direct estimator. We propose a new small area estimator that accounts for sampling variability in the auxiliary information, and derive its properties, in particular showing that it is approximately unbiased. The estimator is applied to predict quantities measured in the U.S. National Health and Nutrition Examination Survey, with auxiliary information from the U.S. National Health Interview Survey. Copyright 2008, Oxford University Press.

Date: 2008
References: Add references at CitEc
Citations View citations in EconPapers (12) Track citations by RSS feed

Downloads: (external link) (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

Access Statistics for this article

Biometrika is currently edited by A C Davison

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().

Page updated 2019-01-21
Handle: RePEc:oup:biomet:v:95:y:2008:i:4:p:919-931