The University of Southampton
University of Southampton Institutional Repository

Capture-recapture modelling for zero-truncated count data allowing for heterogeneity

Capture-recapture modelling for zero-truncated count data allowing for heterogeneity
Capture-recapture modelling for zero-truncated count data allowing for heterogeneity
Capture-recapture modelling is a powerful tool for estimating an elusive target population size. This thesis proposes four new population size estimators allowing for population heterogeneity. The first estimator is developed under the zero-truncated of generalised Poisson distribution (ZTGP), called the MLEGP. The two parameters of the ZTGP are estimated by using a maximum likelihood with the Expectation-Maximisation algorithm (EM algorithm).

The second estimator is the population size estimator under the zero-truncated Conway-Maxwell-Poisson distribution (ZTCMP). The benefits of using the Conway-Maxwell-Poisson distribution (CMP) are that it includes the Bernoulli, Poisson and geometric distribution as special cases. It is also flexible for over- and under-dispersions relative to the original Poisson model. Moreover, the parameter estimates can be achieved by a simple linear regression approach. The uncertainty in estimating variances of the unknown population size under new estimator is studied with analytic and resampling approaches.

The geometric distribution is one of the nested models under the Conway-Maxwell-Poisson distribution, the Turing and the Zelterman estimators are extended for the geometric distribution and its related model, respectively. Variance estimation and confidence intervals are constructed by the normal approximation method.

An uncertainty of variance estimation of population size estimators for single marking capture-recapture data is studied in the final part of the research. Normal approximation and three resample approaches of variance estimation are compared for the Chapman and the Chao estimators. All of the approaches are assessed through simulations, and real data sets are provided as guidance for understanding the methodologies.
Anan, Orasa
b4cd80a9-6873-490a-9c8f-3d7955a00e1b
Anan, Orasa
b4cd80a9-6873-490a-9c8f-3d7955a00e1b
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e

Anan, Orasa (2016) Capture-recapture modelling for zero-truncated count data allowing for heterogeneity. University of Southampton, School of Mathematics, Doctoral Thesis, 298pp.

Record type: Thesis (Doctoral)

Abstract

Capture-recapture modelling is a powerful tool for estimating an elusive target population size. This thesis proposes four new population size estimators allowing for population heterogeneity. The first estimator is developed under the zero-truncated of generalised Poisson distribution (ZTGP), called the MLEGP. The two parameters of the ZTGP are estimated by using a maximum likelihood with the Expectation-Maximisation algorithm (EM algorithm).

The second estimator is the population size estimator under the zero-truncated Conway-Maxwell-Poisson distribution (ZTCMP). The benefits of using the Conway-Maxwell-Poisson distribution (CMP) are that it includes the Bernoulli, Poisson and geometric distribution as special cases. It is also flexible for over- and under-dispersions relative to the original Poisson model. Moreover, the parameter estimates can be achieved by a simple linear regression approach. The uncertainty in estimating variances of the unknown population size under new estimator is studied with analytic and resampling approaches.

The geometric distribution is one of the nested models under the Conway-Maxwell-Poisson distribution, the Turing and the Zelterman estimators are extended for the geometric distribution and its related model, respectively. Variance estimation and confidence intervals are constructed by the normal approximation method.

An uncertainty of variance estimation of population size estimators for single marking capture-recapture data is studied in the final part of the research. Normal approximation and three resample approaches of variance estimation are compared for the Chapman and the Chao estimators. All of the approaches are assessed through simulations, and real data sets are provided as guidance for understanding the methodologies.

PDF
OrasaAnan_Thesis_21_09_2016.pdf - Other
Available under License University of Southampton Thesis Licence.
Download (33MB)

More information

Published date: September 2016
Organisations: University of Southampton, Mathematical Sciences

Identifiers

Local EPrints ID: 402562
URI: https://eprints.soton.ac.uk/id/eprint/402562
PURE UUID: 4f9101fb-1c88-40ef-b72b-0d0a9c3322a1
ORCID for Dankmar Bohning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 01 Dec 2016 16:33
Last modified: 06 Oct 2018 00:32

Export record

Contributors

Author: Orasa Anan
Thesis advisor: Dankmar Bohning ORCID iD
Thesis advisor: Antonello Maruotti

University divisions

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 https://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.

×