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Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures
Abstract
Summary.Agresti (1994,Biometrics50, 494–500) and Norris and Pollock (1996a,Biometrics52, 639–649) suggested using methods of finite mixtures to partition the animals in a closed capture‐recapture experiment into two or more groups with relatively homogeneous capture probabilities. This enabled them to fit the models Mh, Mbh(Norris and Pollock), and Mth(Agresti) of Otis et al. (1978,Wildlife Monographs62, 1–135). In this article, finite mixture partitions of animals and/or samples are used to give a unified linear‐logistic framework for fitting all eight models of Otis et al. by maximum likelihood. Likelihood ratio tests are available for model comparisons. For many data sets, a simple dichotomy of animals is enough to substantially correct for heterogeneity‐induced bias in the estimation of population size, although there is the option of fitting more than two groups if the data warrant it.
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Cites
Nonparametric maximum likelihood estimation of a mixing distribution
Laird, N. (1978) · Journal of the American Statistical Association · doi:10.1080/01621459.1978.10480103 · Cited by 753
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Metadata
- Authors
- Pledger, S.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-06
- Volume
- 56
- Issue
- 2
- Pages
- 434-442
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2030895949
- Cited by
- 539
- References
- 1
- FWCI
- 12.01
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pledger, S. (2000). Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures. Biometrics. https://doi.org/10.1111/j.0006-341x.2000.00434.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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