CAMCR: Computer-Assisted Mixture model analysis for Capture–Recapture count data
CAMCR: Computer-Assisted Mixture model analysis for Capture–Recapture count data
Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.
61-71
Kuhnert, Ronny
8518a8ac-54e5-4117-b66b-66a82fdece7c
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
2009
Kuhnert, Ronny
8518a8ac-54e5-4117-b66b-66a82fdece7c
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Kuhnert, Ronny and Böhning, Dankmar
(2009)
CAMCR: Computer-Assisted Mixture model analysis for Capture–Recapture count data.
AStA Advances in Statistical Analysis, 93 (1), .
(doi:10.1007/s10182-008-0092-z).
Abstract
Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.
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e-pub ahead of print date: 31 October 2008
Published date: 2009
Organisations:
Statistics, Statistical Sciences Research Institute
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Local EPrints ID: 210559
URI: http://eprints.soton.ac.uk/id/eprint/210559
ISSN: 1863-8171
PURE UUID: 03686ae7-80ae-45da-b3fa-34f3c6f177b6
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Date deposited: 09 Feb 2012 14:51
Last modified: 15 Mar 2024 03:39
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Author:
Ronny Kuhnert
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