Bias correction in multiple-systems estimation
Bias correction in multiple-systems estimation
If part of a population is hidden but two or more sources are available that each cover parts of this population, dual- or multiple-system(s) estimation can be applied to estimate this population. For this it is common to use the log-linear model, estimated with maximum likelihood. These maximum likelihood estimates are based on a non-linear model and therefore suffer from finite-sample bias, which can be substantial in case of small samples or a small population size. This problem was recognised by Chapman, who derived an estimator with good small sample properties in case of two available sources. However, he did not derive an estimator for more than two sources. We propose an estimator that is an extension of Chapman's estimator to three or more sources and compare this estimator with other bias-reduced estimators in a simulation study. The proposed estimator performs well, and much better than the other estimators. A real data example on homelessness in the Netherlands shows that our proposed model can make a substantial difference.
stat.ME, stat.AP
Zult, Daan B.
ce9fcd6b-0119-443e-af59-d83a69c11cfa
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Bakker, Bart F.M.
8b086ec4-999b-4966-bef5-f23cfc098fd7
Zult, Daan B.
ce9fcd6b-0119-443e-af59-d83a69c11cfa
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Bakker, Bart F.M.
8b086ec4-999b-4966-bef5-f23cfc098fd7
[Unknown type: UNSPECIFIED]
Abstract
If part of a population is hidden but two or more sources are available that each cover parts of this population, dual- or multiple-system(s) estimation can be applied to estimate this population. For this it is common to use the log-linear model, estimated with maximum likelihood. These maximum likelihood estimates are based on a non-linear model and therefore suffer from finite-sample bias, which can be substantial in case of small samples or a small population size. This problem was recognised by Chapman, who derived an estimator with good small sample properties in case of two available sources. However, he did not derive an estimator for more than two sources. We propose an estimator that is an extension of Chapman's estimator to three or more sources and compare this estimator with other bias-reduced estimators in a simulation study. The proposed estimator performs well, and much better than the other estimators. A real data example on homelessness in the Netherlands shows that our proposed model can make a substantial difference.
Text
2311.01297v1
- Author's Original
More information
e-pub ahead of print date: 2 November 2023
Keywords:
stat.ME, stat.AP
Identifiers
Local EPrints ID: 484123
URI: http://eprints.soton.ac.uk/id/eprint/484123
PURE UUID: 5a7288f6-46de-49a8-bada-8d01b83d97b2
Catalogue record
Date deposited: 10 Nov 2023 17:55
Last modified: 18 Mar 2024 03:25
Export record
Altmetrics
Contributors
Author:
Daan B. Zult
Author:
Bart 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