Unified maximum likelihood estimates for closed capture-recapture models using mixtures
Unified maximum likelihood estimates for closed capture-recapture models using mixtures
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DOI：
10.1111/j.0006-341x.2000.00434.x
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发表时间：
2000-06-01
期刊：
影响因子：
1.9
通讯作者：
Pledger, S
中科院分区：
文献类型：
--
作者：
Pledger, S
Reststi（1994，Biometrics 50，494-500）和Norris and Pollock（1996 a，Biometrics 52，639-649）建议使用有限混合的方法将封闭捕获-再捕获实验中的动物分成两组或更多组，具有相对均匀的捕获概率。这使得他们能够拟合奥蒂斯等人（1978，Wildlife Monographs 62，1-135）的模型M-h、M-bh（Norris和Pollock）和M-th（Polsti）。在这篇文章中，有限的混合物分区的动物和/或样本，给出了一个统一的线性逻辑框架拟合所有八个模型的奥蒂斯等人。似然比检验可用于模型比较。对于许多数据集，动物的简单二分法足以在估计种群大小时基本上校正异质性引起的偏差，尽管如果数据需要，可以选择拟合两个以上的组。
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 M-h, M-bh (Norris and Pollock), and M-th (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.