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A regression estimator for mixed binomial capture-recapture data

A regression estimator for mixed binomial capture-recapture data
A regression estimator for mixed binomial capture-recapture data
Mixed binomial models are frequently used to provide estimates for the unknown size of a partially observed population when capture–recapture data are available through a known, finite, number of identification (sampling) sources. In this context, inherently major problems may be the lack of identifiability of the mixing distribution (Link, 2003) and boundary problems in ML estimation for mixed binomial models (such as the beta-binomial or finite mixture of binomials), see e.g. Dorazio and Royle, 2003 and Dorazio and Royle, 2005. To solve these problems, we introduce a novel regression estimator based on observed ratios of successive capture frequencies. Both simulations and real data examples show that the proposed estimator frequently leads to under-estimate the true population size, but with a smaller bias and a lower variability when compared to other well-known estimators.
beta-binomial, weighted regression, zero-truncation
0378-3758
165-178
Rocchetti, I.
8e3b8dda-a55f-4a4e-b2e6-0c488ecb883d
Alfo, M.
b98112ea-db27-41fb-9389-02df71e2d2b6
Boehning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Rocchetti, I.
8e3b8dda-a55f-4a4e-b2e6-0c488ecb883d
Alfo, M.
b98112ea-db27-41fb-9389-02df71e2d2b6
Boehning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1

Rocchetti, I., Alfo, M. and Boehning, Dankmar (2014) A regression estimator for mixed binomial capture-recapture data. Journal of Statistical Planning and Inference, 145, 165-178. (doi:10.1016/j.jspi.2013.08.010).

Record type: Article

Abstract

Mixed binomial models are frequently used to provide estimates for the unknown size of a partially observed population when capture–recapture data are available through a known, finite, number of identification (sampling) sources. In this context, inherently major problems may be the lack of identifiability of the mixing distribution (Link, 2003) and boundary problems in ML estimation for mixed binomial models (such as the beta-binomial or finite mixture of binomials), see e.g. Dorazio and Royle, 2003 and Dorazio and Royle, 2005. To solve these problems, we introduce a novel regression estimator based on observed ratios of successive capture frequencies. Both simulations and real data examples show that the proposed estimator frequently leads to under-estimate the true population size, but with a smaller bias and a lower variability when compared to other well-known estimators.

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More information

Accepted/In Press date: 2 August 2013
e-pub ahead of print date: 10 August 2013
Published date: February 2014
Keywords: beta-binomial, weighted regression, zero-truncation
Organisations: Primary Care & Population Sciences

Identifiers

Local EPrints ID: 373501
URI: https://eprints.soton.ac.uk/id/eprint/373501
ISSN: 0378-3758
PURE UUID: bbb90e60-879b-4e7d-8bc0-89b2785b787f
ORCID for Dankmar Boehning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 20 Jan 2015 16:17
Last modified: 20 Jul 2019 00:44

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