Dual- and multiple-system estimation with fully and partially observed covariates
Dual- and multiple-system estimation with fully and partially observed covariates
This chapter describes to the size of a population; however, the information about that population is imperfect. However, it is generally agreed that these assumptions are unlikely to hold in human populations. Including covariates in log-linear models of population registers improves population-size estimates for two reasons. Firstly, it takes account of heterogeneity of inclusion probabilities over the levels of the covariate; and secondly, it subdivides the estimated population by the levels of the covariates, giving insight into characteristics of individuals that are not included in any of the registers. The chapter discusses the invariance of population-size estimates derived from log-linear models that include covariates, with the same covariates available in all the registers. Creating a population breakdown by passive covariates through an appropriate collapsible model is an elegant way to tackle this important practical problem. The chapter considers how the existence of invariant population sizes can help in choosing an appropriate model.
137-168
van Der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Smith, Paul A.
a2548525-4f99-4baf-a4d0-2b216cce059c
Whittaker, Joe
f706fc63-fc7f-4f24-9833-e2c8f69965c4
Cruyff, Maarten
68bcfa19-3d85-4b0f-a6a4-6e148b265f19
Bakker, Bart F.M.
75cc130a-157a-4b06-a5ea-92a6457d806f
2019
van Der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Smith, Paul A.
a2548525-4f99-4baf-a4d0-2b216cce059c
Whittaker, Joe
f706fc63-fc7f-4f24-9833-e2c8f69965c4
Cruyff, Maarten
68bcfa19-3d85-4b0f-a6a4-6e148b265f19
Bakker, Bart F.M.
75cc130a-157a-4b06-a5ea-92a6457d806f
van Der Heijden, Peter G.M., Smith, Paul A., Whittaker, Joe, Cruyff, Maarten and Bakker, Bart F.M.
(2019)
Dual- and multiple-system estimation with fully and partially observed covariates.
In,
Zhang, Li-Chun and Chambers, Raymond L.
(eds.)
Analysis of Integrated Data.
(Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
1 ed.
Chapman and Hall/CRC, .
(doi:10.1201/9781315120416-7).
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Book Section
Abstract
This chapter describes to the size of a population; however, the information about that population is imperfect. However, it is generally agreed that these assumptions are unlikely to hold in human populations. Including covariates in log-linear models of population registers improves population-size estimates for two reasons. Firstly, it takes account of heterogeneity of inclusion probabilities over the levels of the covariate; and secondly, it subdivides the estimated population by the levels of the covariates, giving insight into characteristics of individuals that are not included in any of the registers. The chapter discusses the invariance of population-size estimates derived from log-linear models that include covariates, with the same covariates available in all the registers. Creating a population breakdown by passive covariates through an appropriate collapsible model is an elegant way to tackle this important practical problem. The chapter considers how the existence of invariant population sizes can help in choosing an appropriate model.
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Published date: 2019
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Local EPrints ID: 430193
URI: http://eprints.soton.ac.uk/id/eprint/430193
PURE UUID: a730ffd0-c5f0-41fd-a397-4a8a7f47aafc
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Date deposited: 16 Apr 2019 16:30
Last modified: 16 Mar 2024 04:19
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Contributors
Author:
Joe Whittaker
Author:
Maarten Cruyff
Author:
Bart F.M. Bakker
Editor:
Li-Chun Zhang
Editor:
Raymond L. Chambers
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