The University of Southampton
University of Southampton Institutional Repository

Upper bound estimators of the population size based on ordinal models for capture-recapture experiments

Upper bound estimators of the population size based on ordinal models for capture-recapture experiments
Upper bound estimators of the population size based on ordinal models for capture-recapture experiments

Capture-recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture-recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large-scale simulation experiment based on the scheme discussed by Pledger.

capture-recapture experiments, frequency of frequencies distribution, ordinal data, population size estimation, upper bound
1541-0420
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Alfò, Marco
dad67665-30d4-4e5a-abc7-d1a9005bae7b
Rocchetti, Irene
860f3ca0-8363-4fb4-b306-9a70e74bb663
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Alfò, Marco
dad67665-30d4-4e5a-abc7-d1a9005bae7b
Rocchetti, Irene
860f3ca0-8363-4fb4-b306-9a70e74bb663

Bohning, Dankmar, Alfò, Marco and Rocchetti, Irene (2020) Upper bound estimators of the population size based on ordinal models for capture-recapture experiments. Biometrics. (doi:10.1111/biom.13265).

Record type: Article

Abstract

Capture-recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture-recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large-scale simulation experiment based on the scheme discussed by Pledger.

Text
paperOCR - Accepted Manuscript
Available under License Other.
Download (361kB)

More information

Accepted/In Press date: 24 March 2020
e-pub ahead of print date: 13 April 2020
Keywords: capture-recapture experiments, frequency of frequencies distribution, ordinal data, population size estimation, upper bound

Identifiers

Local EPrints ID: 438989
URI: http://eprints.soton.ac.uk/id/eprint/438989
ISSN: 1541-0420
PURE UUID: 3513309c-6b36-4aae-85b7-2abbd4c8e23e
ORCID for Dankmar Bohning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 31 Mar 2020 16:31
Last modified: 06 Jun 2024 01:49

Export record

Altmetrics

Contributors

Author: Dankmar Bohning ORCID iD
Author: Marco Alfò
Author: Irene Rocchetti

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.

×