Mean‐squared error estimation in transformed Fay–Herriot models

Eric V. Slud and Tapabrata Maiti

Journal of the Royal Statistical Society Series B, 2006, vol. 68, issue 2, 239-257

Abstract: Summary. The problem of accurately estimating the mean‐squared error of small area estimators within a Fay–Herriot normal error model is studied theoretically in the common setting where the model is fitted to a logarithmically transformed response variable. For bias‐corrected empirical best linear unbiased predictor small area point estimators, mean‐squared error formulae and estimators are provided, with biases of smaller order than the reciprocal of the number of small areas. The performance of these mean‐squared error estimators is illustrated by a simulation study and a real data example relating to the county level estimation of child poverty rates in the US Census Bureau's on‐going ‘Small area income and poverty estimation’ project.

Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
https://doi.org/10.1111/j.1467-9868.2006.00542.x

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: https://EconPapers.repec.org/RePEc:bla:jorssb:v:68:y:2006:i:2:p:239-257

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().