The University of Southampton
University of Southampton Institutional Repository

Sensitivity analysis and calibration of population size estimates obtained with the zero-truncated Poisson regression model

Sensitivity analysis and calibration of population size estimates obtained with the zero-truncated Poisson regression model
Sensitivity analysis and calibration of population size estimates obtained with the zero-truncated Poisson regression model
Zero-truncated regression models for count data can be used to estimate the size of an elusive population. A frequently encountered problem is that the Poisson model underestimates the population size due to unobserved heterogeneity, while the negative binomial model is not identified. A sensitivity analysis using the negative binomial model with fixed dispersion parameter might provide inside in the robustness of the population size estimate against unobserved heterogeneity, but as yet there is no method to determine realistic values for the dispersion parameter. This article introduces an R-squared measure and the use of the Pearson dispersion statistic to alleviate this problem. As a spin-off, a method is proposed for calibration of population size estimates in monitoring studies where the number of covariates varies over the measurement occasions. The performance of these methods is evaluated in simulation studies, and is illustrated on a population of drunk drivers.
1471-082X
361–373
Cruyff, M.J.
6e4f416c-71cd-4691-a0a2-883ad27aa13b
van der Heijden, P.G.M.
85157917-3b33-4683-81be-713f987fd612
Cruyff, M.J.
6e4f416c-71cd-4691-a0a2-883ad27aa13b
van der Heijden, P.G.M.
85157917-3b33-4683-81be-713f987fd612

Cruyff, M.J. and van der Heijden, P.G.M. (2014) Sensitivity analysis and calibration of population size estimates obtained with the zero-truncated Poisson regression model. Statistical Modelling, 14 (5), 361–373. (doi:10.1177/1471082X13511168).

Record type: Article

Abstract

Zero-truncated regression models for count data can be used to estimate the size of an elusive population. A frequently encountered problem is that the Poisson model underestimates the population size due to unobserved heterogeneity, while the negative binomial model is not identified. A sensitivity analysis using the negative binomial model with fixed dispersion parameter might provide inside in the robustness of the population size estimate against unobserved heterogeneity, but as yet there is no method to determine realistic values for the dispersion parameter. This article introduces an R-squared measure and the use of the Pearson dispersion statistic to alleviate this problem. As a spin-off, a method is proposed for calibration of population size estimates in monitoring studies where the number of covariates varies over the measurement occasions. The performance of these methods is evaluated in simulation studies, and is illustrated on a population of drunk drivers.

Text
2014 SMJ.pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: 16 July 2014
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 369730
URI: http://eprints.soton.ac.uk/id/eprint/369730
ISSN: 1471-082X
PURE UUID: 8c9c191f-08c1-4192-9e2d-28b0e31e641d
ORCID for P.G.M. van der Heijden: ORCID iD orcid.org/0000-0002-3345-096X

Catalogue record

Date deposited: 06 Oct 2014 11:46
Last modified: 15 Mar 2024 03:46

Export record

Altmetrics

Contributors

Author: M.J. Cruyff

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.

×