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On the Turing estimator in capture–recapture count data under the geometric distribution

On the Turing estimator in capture–recapture count data under the geometric distribution
On the Turing estimator in capture–recapture count data under the geometric distribution
We introduce an estimator for an unknown population size in a capture–recapture framework where the count of identifications follows a geometric distribution. This can be thought of as a Poisson count adjusted for exponentially distributed heterogeneity. As a result, a new Turing-type estimator under the geometric distribution is obtained. This estimator can be used in many real life situations of capture–recapture, in which the geometric distribution is more appropriate than the Poisson. The proposed estimator shows a behavior comparable to the maximum likelihood one, on both simulated and real data. Its asymptotic variance is obtained by applying a conditional technique and its empirical behavior is investigated through a large-scale simulation study. Comparisons with other well-established estimators are provided. Empirical applications, in which the population size is known, are also included to further corroborate the simulation results.
10.1007%2Fs00184-018-0695-7
0026-1335
Anan, Orasa
b4cd80a9-6873-490a-9c8f-3d7955a00e1b
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
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 (2018) On the Turing estimator in capture–recapture count data under the geometric distribution. Metrika. (doi:10.1007%2Fs00184-018-0695-7).

Record type: Article

Abstract

We introduce an estimator for an unknown population size in a capture–recapture framework where the count of identifications follows a geometric distribution. This can be thought of as a Poisson count adjusted for exponentially distributed heterogeneity. As a result, a new Turing-type estimator under the geometric distribution is obtained. This estimator can be used in many real life situations of capture–recapture, in which the geometric distribution is more appropriate than the Poisson. The proposed estimator shows a behavior comparable to the maximum likelihood one, on both simulated and real data. Its asymptotic variance is obtained by applying a conditional technique and its empirical behavior is investigated through a large-scale simulation study. Comparisons with other well-established estimators are provided. Empirical applications, in which the population size is known, are also included to further corroborate the simulation results.

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More information

Accepted/In Press date: 26 October 2018
e-pub ahead of print date: 12 November 2018
Published date: 12 November 2018

Identifiers

Local EPrints ID: 426131
URI: https://eprints.soton.ac.uk/id/eprint/426131
ISSN: 0026-1335
PURE UUID: 85b31be3-fb79-4fd4-b935-f1b87ef54b5c
ORCID for Dankmar Bohning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 15 Nov 2018 17:30
Last modified: 14 Mar 2019 01:37

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