A linkage error correction model for population size estimation with multiple sources
A linkage error correction model for population size estimation with multiple sources
A new method is described to do population size estimation, while linkage of sources occurs with errors. Our model is derived from a linkage error correction model introduced by Ding and Fienberg (1994). They show how to use linkage probabilities to correct the capture - recapture estimator for linkage errors, but only in the case of two sources and no covariates. A generalisation is proposed by incorporating the Ding & Fienberg model into the standard log - linear modelling approach used in multiple - recapture estimation. We show how the method performs in a simulation study with data that resemble real data.
Van Der Heijden, Peter
85157917-3b33-4683-81be-713f987fd612
21 August 2019
Van Der Heijden, Peter
85157917-3b33-4683-81be-713f987fd612
Van Der Heijden, Peter
(2019)
A linkage error correction model for population size estimation with multiple sources.
62nd World Statistics Congress, , Kuala Lumpur, Malaysia.
19 - 23 Aug 2019.
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Conference or Workshop Item
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Abstract
A new method is described to do population size estimation, while linkage of sources occurs with errors. Our model is derived from a linkage error correction model introduced by Ding and Fienberg (1994). They show how to use linkage probabilities to correct the capture - recapture estimator for linkage errors, but only in the case of two sources and no covariates. A generalisation is proposed by incorporating the Ding & Fienberg model into the standard log - linear modelling approach used in multiple - recapture estimation. We show how the method performs in a simulation study with data that resemble real data.
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Published date: 21 August 2019
Venue - Dates:
62nd World Statistics Congress, , Kuala Lumpur, Malaysia, 2019-08-19 - 2019-08-23
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Local EPrints ID: 436665
URI: http://eprints.soton.ac.uk/id/eprint/436665
PURE UUID: 164b8b85-7508-4e11-a9d7-b9ef246f5f4c
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Date deposited: 20 Dec 2019 17:52
Last modified: 17 Mar 2024 03:31
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