The University of Southampton
University of Southampton Institutional Repository

Small area estimation under informative probability sampling of areas and within the selected areas

Record type: Article

In this article we show how to predict small area means and obtain valid MSE estimators and confidence intervals when the areas represented in the sample are sampled with unequal probabilities that are possibly related to the true (unknown) area means, and the sampling of units within the selected areas is with probabilities that are possibly related to the outcome values. Ignoring the effects of the sampling process on the distribution of the observed outcomes in such cases may bias the inference very severely. Classical design based inference that uses the randomization distribution of probability weighted estimators cannot be applied for predicting the means of nonsampled areas. We propose simple test statistics for testing the informativeness of the selection of the areas and the sampling of units within the selected areas. The proposed procedures are illustrated by a simulation study and a real application of estimating mean body mass index in counties of the U.S.A, using data from the NHANES III survey.

PDF Pfeffermann_Sverchkov.pdf - Accepted Manuscript
Download (379kB)

Citation

Pfeffermann, Danny and Sverchkov, Michail (2007) Small area estimation under informative probability sampling of areas and within the selected areas Journal of the American Statistical Association, 102, (480), pp. 1427-1439. (doi:10.1198/016214507000001094).

More information

Submitted date: August 2007
Published date: 12 November 2007
Keywords: body mass index, bootstrap, design based inference, sample distribution, sample-complement distribution, sampling weights

Identifiers

Local EPrints ID: 48099
URI: http://eprints.soton.ac.uk/id/eprint/48099
ISSN: 0162-1459
PURE UUID: 98ee8f0b-635b-4fab-8248-7844ec4f3f29

Catalogue record

Date deposited: 28 Aug 2007
Last modified: 17 Jul 2017 15:00

Export record

Altmetrics


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×