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
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
1 September 2015
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).
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
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
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