Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution
Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution
The purpose of the study is to estimate the population size under a truncated count model that accounts for heterogeneity. The proposed estimator is based on the Conway–Maxwell–Poisson distribution. The benefit of using the Conway–Maxwell–Poisson distribution is that it includes the Bernoulli, the Geometric and the Poisson distributions as special cases and, furthermore, allows for heterogeneity. Parameter estimates can be obtained by exploiting the ratios of successive frequency counts in a weighted linear regression framework. The results of the comparisons with Turing’s, the maximum likelihood Poisson, Zelterman’s and Chao’s estimators reveal that our proposal can be beneficially used. Furthermore, our proposal outperforms its competitors under all heterogeneous settings. The empirical examples consider the homogeneous case and several heterogeneous cases, each with its own features, and provide interesting insights on the behavior of the estimators
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Anan, Orasa
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Bohning, Dankmar
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Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Anan, Orasa
b4cd80a9-6873-490a-9c8f-3d7955a00e1b
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Anan, Orasa, Bohning, Dankmar and Maruotti, Antonello
(2016)
Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution.
Statistical Methods & Applications, .
(doi:10.1007/s10260-016-0358-7).
Abstract
The purpose of the study is to estimate the population size under a truncated count model that accounts for heterogeneity. The proposed estimator is based on the Conway–Maxwell–Poisson distribution. The benefit of using the Conway–Maxwell–Poisson distribution is that it includes the Bernoulli, the Geometric and the Poisson distributions as special cases and, furthermore, allows for heterogeneity. Parameter estimates can be obtained by exploiting the ratios of successive frequency counts in a weighted linear regression framework. The results of the comparisons with Turing’s, the maximum likelihood Poisson, Zelterman’s and Chao’s estimators reveal that our proposal can be beneficially used. Furthermore, our proposal outperforms its competitors under all heterogeneous settings. The empirical examples consider the homogeneous case and several heterogeneous cases, each with its own features, and provide interesting insights on the behavior of the estimators
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Accepted/In Press date: 16 March 2016
e-pub ahead of print date: 18 April 2016
Organisations:
Statistics, Faculty of Health Sciences, Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 393209
URI: http://eprints.soton.ac.uk/id/eprint/393209
ISSN: 1618-2510
PURE UUID: f14f9878-dd44-425b-961f-670e4dadfcf4
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Date deposited: 22 Apr 2016 12:54
Last modified: 15 Mar 2024 03:39
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Contributors
Author:
Orasa Anan
Author:
Antonello Maruotti
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