The University of Southampton
University of Southampton Institutional Repository

Sensitivity of population size estimation for violating parametric assumptions in log linear models

Sensitivity of population size estimation for violating parametric assumptions in log linear models
Sensitivity of population size estimation for violating parametric assumptions in log linear models
An important quality aspect of censuses is the degree of coverage of the population. When administrative registers are available undercoverage can be estimated via capture-recapture methodology. The standard approach uses the log-linear model that relies on the assumption that being in the first register is independent of being in the second register. In models using covariates, this assumption of independence is relaxed into independence conditional on covariates. In this article we describe, in a general setting, how sensitivity analyses can be carried out to assess the robustness of the population size estimate. We make use of log-linear Poisson regression using an offset, to simulate departure from the model. This approach can be extended to the case where we have covariates observed in both registers, and to a model with covariates observed in only one register. The robustness of the population size estimate is a function of implied coverage: as implied coverage is low the robustness is low. We conclude that it is important for researchers to investigate and report the estimated robustness of their population size estimate for quality reasons. Extensions are made to log-linear modeling in case of more than two registers and the multiplier method
0282-423X
Gerritse, S.
2719e1ef-01ae-4a90-837b-c9d7712aa31b
van der Heijden, P.G.M.
85157917-3b33-4683-81be-713f987fd612
Bakker, B.F.M.
dd17ff6b-e10a-42a0-8592-beafb65640d7
Gerritse, S.
2719e1ef-01ae-4a90-837b-c9d7712aa31b
van der Heijden, P.G.M.
85157917-3b33-4683-81be-713f987fd612
Bakker, B.F.M.
dd17ff6b-e10a-42a0-8592-beafb65640d7

Gerritse, S., van der Heijden, P.G.M. and Bakker, B.F.M. (2015) Sensitivity of population size estimation for violating parametric assumptions in log linear models. Journal of Official Statistics, 31 (3). (doi:10.1515/jos-2015-0022).

Record type: Article

Abstract

An important quality aspect of censuses is the degree of coverage of the population. When administrative registers are available undercoverage can be estimated via capture-recapture methodology. The standard approach uses the log-linear model that relies on the assumption that being in the first register is independent of being in the second register. In models using covariates, this assumption of independence is relaxed into independence conditional on covariates. In this article we describe, in a general setting, how sensitivity analyses can be carried out to assess the robustness of the population size estimate. We make use of log-linear Poisson regression using an offset, to simulate departure from the model. This approach can be extended to the case where we have covariates observed in both registers, and to a model with covariates observed in only one register. The robustness of the population size estimate is a function of implied coverage: as implied coverage is low the robustness is low. We conclude that it is important for researchers to investigate and report the estimated robustness of their population size estimate for quality reasons. Extensions are made to log-linear modeling in case of more than two registers and the multiplier method

Text
jos-2015-0022.pdf - Version of Record
Available under License Creative Commons Attribution.
Download (302kB)

More information

Accepted/In Press date: 1 October 2014
Published date: 1 September 2015
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 381216
URI: http://eprints.soton.ac.uk/id/eprint/381216
ISSN: 0282-423X
PURE UUID: 952d903b-94c2-40a3-883d-3db22a0bd7c6
ORCID for P.G.M. van der Heijden: ORCID iD orcid.org/0000-0002-3345-096X

Catalogue record

Date deposited: 01 Oct 2015 08:55
Last modified: 15 Mar 2024 03:46

Export record

Altmetrics

Contributors

Author: S. Gerritse
Author: B.F.M. Bakker

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.

×