Full Open Population Capture–Recapture Models With Individual Covariates
Full Open Population Capture–Recapture Models With Individual Covariates
复制标题
完全开放的群体捕获-带有个体协变量的重新捕获模型
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
R. Barker
中科院分区：
文献类型：
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作者：
Matthew R. Schofield;R. Barker
捕获-再捕获数据的传统分析基于显式整合所有缺失数据的似然函数。我们使用一个完整的数据可能性（CDL），以显示如何广泛的捕获-再捕获模型可以很容易地使用现成的软件JAGS/BUGS，即使有个别特定的随时间变化的协变量拟合。我们所描述的模型扩展了那些以第一次捕获为条件的模型，包括丰度参数，或与丰度相关的参数，如种群规模，出生率或寿命。CDL的使用意味着任何缺失的数据，包括不确定的个体协变量，都可以包含在模型中，而不需要定制的似然函数。这种方法还有助于人口统计学兴趣的建模过程，而不是由不可重复的缺失数据引起的复杂性。我们使用两个例子来说明，（i）在完全稳健设计中存在删失时变个体协变量的开放群体建模，以及（ii）在部分观察到的分类变量的存在下的完全开放群体多状态建模。这篇文章的补充材料可以在网上找到。
Traditional analyses of capture–recapture data are based on likelihood functions that explicitly integrate out all missing data. We use a complete data likelihood (CDL) to show how a wide range of capture–recapture models can be easily fitted using readily available software JAGS/BUGS even when there are individual-specific time-varying covariates. The models we describe extend those that condition on first capture to include abundance parameters, or parameters related to abundance, such as population size, birth rates or lifetime. The use of a CDL means that any missing data, including uncertain individual covariates, can be included in models without the need for customized likelihood functions. This approach also facilitates modeling processes of demographic interest rather than the complexities caused by non-ignorable missing data. We illustrate using two examples, (i) open population modeling in the presence of a censored time-varying individual covariate in a full robust design, and (ii) full open population multi-state modeling in the presence of a partially observed categorical variable. Supplemental materials for this article are available online.