基于注册数据的捕获-重捕隐马尔可夫模型框架用于人口规模和动态推断
A capture-recapture hidden Markov model framework for register-based inference of population size and dynamics
AI总结：
本文提出一种可扩展框架，利用隐马尔可夫模型推断注册数据中的人口规模和动态，考虑临时迁出、多注册源误差及个体特征影响。
摘要：
准确推断人口动态，如迁移和人口规模变化，对政策制定、资源分配和人口研究至关重要。传统普查成本高、频率低且不及时，导致许多国家采用基于注册的数据方法替代或补充。主要挑战是这些注册不完整：即使个体存在，其活动可能不生成特定注册记录，导致假阴性观测误差。相反，某些注册来自行政或家庭层面过程，导致个体可能在注册中出现尽管实际不存在，产生假阳性观测误差。现有方法常依赖任意决策忽略误差类型、仅推断人口快照而非动态，或计算速度太慢。本文提出一个可扩展框架，基于Cormack-Jolly-Seber型捕获-重捕模型，以隐马尔可夫模型形式构建。推断采用最大似然估计，不确定性通过Bag of Little Bootstraps量化。模型考虑临时迁出，整合任意数量可能相互作用的注册源，考虑两种误差类型，并允许观测概率随个体特征和不可观测异质性变化。通过瑞典人口注册数据应用，其中过度覆盖（个体被注册为生活在该国但已不在）提供示例。应用获得人口动态和个体轨迹的新见解。
英文摘要：
Accurate inference on population dynamics, such as migration and changes in population size, is essential for policymaking, resource allocation and demographic research. Traditional censuses are expensive, infrequent and not timely, leading many countries to adopt register-based approaches to replace or complement them. A primary challenge is that such registers are incomplete: even when individuals are present, their activities may not generate records in specific registers, resulting in false negative observation error. Conversely, some registers arise from administrative or household-level processes, so that individuals may appear in registers despite being absent, leading to false positive observation error. Existing approaches often either rely on ad-hoc decisions that ignore one or both error types, offer inference on population snapshots but not dynamics, or are computationally too slow for practical use. We propose a scalable framework for inferring population size and dynamics from register data, building on Cormack-Jolly-Seber type capture-recapture models formulated as hidden Markov models. Inference is carried out using maximum likelihood estimation, with uncertainty quantified via the Bag of Little Bootstraps. The model accounts for temporary emigration, incorporates an arbitrary number of possibly interacting registers subject to both error types, and allows observation probabilities to vary with individual characteristics and unobservable heterogeneity. We illustrate the approach using Swedish population registers, where overcoverage - individuals registered as living in the country although they are no longer present - provides a motivating example. The application yields new insights into population dynamics and individual trajectories.
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