The University of Southampton
University of Southampton Institutional Repository

M-Quantile Models for Small Area Estimation

Record type: Monograph (Working Paper)

Small area estimation techniques are employed when sample data are insufficient for acceptably precise direct estimation in domains of interest. These techniques typically rely on regression models that use both covariates and random effects to explain variation between domains. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. We describe a new 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 avoids the problems associated with specification of random effects, allowing inter-domain differences to be characterized by the variation of area-specific M-quantile coefficients. The proposed approach is easily made robust against outlying data values and can be adapted for estimation of a wide range of area specific parameters, including that of the quantiles of the distribution of the target variable in the different small areas. Results from two simulation studies comparing the performance of the M-quantile modelling approach with more traditional mixed model approaches are also provided.

PDF 14077-01.pdf - Other
Download (297kB)

Citation

Chambers, Ray and Tzavidis, Nikos (2005) M-Quantile Models for Small Area Estimation , Southampton, UK Southampton Statistical Sciences Research Institute 33pp. (S3RI Methodology Working Papers, M05/07).

More information

Published date: 25 January 2005

Identifiers

Local EPrints ID: 14077
URI: http://eprints.soton.ac.uk/id/eprint/14077
PURE UUID: 11689cc1-8375-4684-abba-533c29e3f4c6

Catalogue record

Date deposited: 26 Jan 2005
Last modified: 17 Jul 2017 16:58

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.

×