Multistate Mark-Recapture Model Selection Using Score Tests
Multistate Mark-Recapture Model Selection Using Score Tests
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DOI：
10.1111/j.1541-0420.2010.01421.x
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发表时间：
2011-03-01
期刊：
影响因子：
1.9
通讯作者：
Morgan, Byron J. T.
中科院分区：
文献类型：
--
作者：
McCrea, Rachel S.;Morgan, Byron J. T.
尽管多状态标记-再捕获模型被认为是重要的，但它们缺乏一个简单的模型选择程序。本文提出并评价了一种利用分数检验为多态标记重获数据选择合适模型的逐步方法。只有数据支持的模型才需要拟合，这样就不需要考虑参数太多的过于复杂的模型结构。通常只对少量模型进行拟合，并且该程序还能够识别参数冗余和接近冗余的模型。仿真结果表明了该方法的良好性能，并以加拿大鹅的三个区域的数据集为例说明了该方法。在本例中，它标识了一个新模型，该模型比之前为该应用程序考虑的最佳模型简单得多。
Although multistate mark-recapture models are recognized as important, they lack a simple model-selection procedure. This article proposes and evaluates a step-up approach to select appropriate models for multistate mark-recapture data using score tests. Only models supported by the data require fitting, so that over-complicated model structures with too many parameters do not need to be considered. Typically only a small number of models are fitted, and the procedure is also able to identify parameter-redundant and near-redundant models. The good performance of the technique is demonstrated using simulation, and the approach is illustrated on a three-region Canada goose data set. In this case, it identifies a new model that is much simpler than the best model previously considered for this application.