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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
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
0964-1998
453-479
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
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), 453-479. (doi:10.1111/rssa.12123).

Record type: Article

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.

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M_quantile_regression_JRSS_A_NT_NS_RC.pdf - Accepted Manuscript
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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: https://eprints.soton.ac.uk/id/eprint/378403
ISSN: 0964-1998
PURE UUID: c7d3303a-07f0-401c-a5fa-5e225b36a394
ORCID for Nikos Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095

Catalogue record

Date deposited: 30 Jun 2015 10:57
Last modified: 15 Aug 2019 00:47

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