The University of Southampton
University of Southampton Institutional Repository

Bias Adjusted Estimation for Small Areas with Outlying Values

Tzavidis, Nikos and Chambers, Ray (2006) Bias Adjusted Estimation for Small Areas with Outlying Values , Southampton, UK Southampton Statistical Sciences Research Institute 26pp. (S3RI Methodology Working Papers, M06/09).

Record type: Monograph (Working Paper)

Abstract

Small area estimation techniques typically rely on regression models that use both covariates and random effects to explain between domain variation. Chambers and Tzavidis (2006) describe a novel approach to small area estimation that is based on modelling quantile-like parameters of the conditional distribution of the target variable given the covariates. This is an outlier robust approach that avoids conventional Gaussian assumptions and the problems associated with specification of random effects, allowing inter-domain differences to be characterized by the variation of area-specific M-quantile coefficients. These authors observed, however, that M-quantile estimates of small area means are biased with the magnitude of the bias being related to the presence of outliers in the data. In this paper we propose a bias adjustment to the M-quantile small area estimator of the mean that is based on representing this estimator as a functional of the small area distribution function. The method is then generalized for estimating other quantiles of the distribution function in a small area. The effect of this bias adjustment on small area estimation with random effects models in the presence of model misspecification is also examined.

PDF 38976-01.pdf - Author's Original
Download (242kB)

More information

Published date: 14 June 2006

Identifiers

Local EPrints ID: 38976
URI: http://eprints.soton.ac.uk/id/eprint/38976
PURE UUID: f8ed13d9-6df3-4140-a6fe-3a7981c90fd3

Catalogue record

Date deposited: 14 Jun 2006
Last modified: 17 Jul 2017 15:38

Export record

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.

×