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      Unified maximum likelihood estimates for closed capture-recapture models using mixtures.

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      Biometrics
      Wiley

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          Abstract

          Agresti (1994, Biometrics 50, 494-500) and Norris and Pollock (1996a, Biometrics 52, 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 Monographs 62, 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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          Author and article information

          Journal
          Biometrics
          Biometrics
          Wiley
          0006-341X
          0006-341X
          Jun 2000
          : 56
          : 2
          Affiliations
          [1 ] School of Mathematical and Computing Sciences, Victoria University of Wellington, New Zealand. shirley.pledger@vuw.ac.nz
          Article
          10.1111/j.0006-341x.2000.00434.x
          10877301
          d96c199c-7ea9-4042-a227-21144ae60cc6
          History

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