A New Strategy for Diagnostic Model Assessment in Capture-Recapture
A New Strategy for Diagnostic Model Assessment in Capture-Recapture
复制标题
捕获-再捕获诊断模型评估的新策略
DOI：
10.1111/rssc.12197
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
McCrea R
中科院分区：
文献类型：
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作者：
McCrea R
捕获-再捕获和得分测试中使用的诊断测试的共同点是，从简单的基础模型开始，可以询问数据以确定是否支持更复杂的参数结构。目前的建议建议，诊断测试是作为一个模型选择步骤的先驱。我们发现，某些众所周知的诊断测试，检查适合的捕获-再捕获模型的数据，实际上是得分测试。由于这种直接的关系，我们研究了一种新的战略模型评估相结合的诊断偏离基本模型的假设，一个逐步的模型选择，所有的基础上得分测试。我们研究了这种方法的力量，以检测缺乏模型拟合的常见原因，并通过模拟比较这种新策略与现有建议的性能。我们提出了激励的例子与真实的数据，额外的灵活性的分数测试结果在一个改进的性能相比，诊断测试。
Common to both diagnostic tests used in capture–recapture and score tests is the idea that starting from a simple base model it is possible to interrogate data to determine whether more complex parameter structures will be supported. Current recommendations advise that diagnostic tests are performed as a precursor to a model selection step. We show that certain well-known diagnostic tests for examining the fit of capture–recapture models to data are in fact score tests. Because of this direct relationship we investigate a new strategy for model assessment which combines the diagnosis of departure from basic model assumptions with a step-up model selection, all based on score tests. We investigate the power of such an approach to detect common reasons for lack of model fit and compare the performance of this new strategy with the existing recommendations by using simulation. We present motivating examples with real data for which the extra flexibility of score tests results in an improved performance compared with diagnostic tests.