Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK
Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK
A new semiparametric and robust approach to small area estimation for discrete outcomes is proposed. The methodology represents an efficient and easily computed alternative to prediction by using a generalized linear mixed model and is based on an extension of M-quantile regression. In addition, two estimators of the prediction mean-squared error are described: one based on Taylor linearization and another based on the block bootstrap. The methodology proposed is applied to UK annual Labour Force Survey data for estimating the proportion of the unemployed in local authorities in the UK. The properties of estimators are further empirically assessed in model-based simulations.
generalized linear mixed model, m-estimation, m-quantiles, robust inference, uk labour force survey
453-479
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
February 2016
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Chambers, Ray, Salvati, Nicola and Tzavidis, Nikos
(2016)
Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK.
Journal of the Royal Statistical Society: Series A (Statistics in Society), 179 (2), .
(doi:10.1111/rssa.12123).
Abstract
A new semiparametric and robust approach to small area estimation for discrete outcomes is proposed. The methodology represents an efficient and easily computed alternative to prediction by using a generalized linear mixed model and is based on an extension of M-quantile regression. In addition, two estimators of the prediction mean-squared error are described: one based on Taylor linearization and another based on the block bootstrap. The methodology proposed is applied to UK annual Labour Force Survey data for estimating the proportion of the unemployed in local authorities in the UK. The properties of estimators are further empirically assessed in model-based simulations.
Text
M_quantile_regression_JRSS_A_NT_NS_RC.pdf
- Accepted Manuscript
More information
e-pub ahead of print date: 18 May 2015
Published date: February 2016
Keywords:
generalized linear mixed model, m-estimation, m-quantiles, robust inference, uk labour force survey
Organisations:
Social Statistics & Demography
Identifiers
Local EPrints ID: 378403
URI: http://eprints.soton.ac.uk/id/eprint/378403
ISSN: 0964-1998
PURE UUID: c7d3303a-07f0-401c-a5fa-5e225b36a394
Catalogue record
Date deposited: 30 Jun 2015 10:57
Last modified: 15 Mar 2024 03:11
Export record
Altmetrics
Contributors
Author:
Ray Chambers
Author:
Nicola Salvati
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
