论文标题
嵌套的dirichlet过程，用于从多列表重新捕获数据中估算人口规模的过程
Nested Dirichlet Process For Population Size Estimation From Multi-list Recapture Data
论文作者
论文摘要
当捕获模式随着时间和位置而不同时，响应模式的异质性对于估计来自多个重新捕获数据的封闭种群的大小很重要。在本文中，我们将非参数一层潜在类模型扩展为Manrique-Vallier（2016）提出的多个重新捕获数据的非参数一层潜在模型，并将其建模单个异质性和第二层建模位置时间差异的嵌套潜在类模型。具有相似记录模式的位置时间组在同一顶层潜在类中，每个顶层类中的个人都取决于。嵌套的潜在类模型将层次异质性纳入建模中，以从多列表重新捕获数据中估算人口规模。这种方法会导致更准确的人口规模估计和降低的不确定性。我们将方法应用于估计叙利亚冲突的伤亡。
Heterogeneity of response patterns is important in estimating the size of a closed population from multiple recapture data when capture patterns are different over time and location. In this paper, we extend the non-parametric one layer latent class model for multiple recapture data proposed by Manrique-Vallier (2016) to a nested latent class model with the first layer modeling individual heterogeneity and the second layer modeling location-time differences. Location-time groups with similar recording patterns are in the same top layer latent class and individuals within each top layer class are dependent. The nested latent class model incorporates hierarchical heterogeneity into the modeling to estimate population size from multi-list recapture data. This approach leads to more accurate population size estimation and reduced uncertainty. We apply the method to estimating casualties from the Syrian conflict.
