A HIERARCHICAL BAYESIAN APPROACH TO RECORD LINKAGE AND POPULATION SIZE PROBLEMS

• 成果类型: Article
• 署名作者: Tancredi, Andrea; Liseo, Brunero
• 署名单位: Sapienza University Rome
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/10-AOAS447
• 发表日期: 2011
• 页码: 1553-1585
• 关键词: models; census; methodology; generation; alignment; error

摘要

We propose and illustrate a hierarchical Bayesian approach for matching statistical records observed on different occasions. We show how this model can be profitably adopted both in record linkage problems and in capture-recapture setups, where the size of a finite population is the real object of interest. There are at least two important differences between the proposed model-based approach and the current practice in record linkage. First, the statistical model is built up on the actually observed categorical variables and no reduction (to 0-1 comparisons) of the available information takes place. Second, the hierarchical structure of the model allows a two-way propagation of the uncertainty between the parameter estimation step and the matching procedure so that no plug-in estimates are used and the correct uncertainty is accounted for both in estimating the population size and in performing the record linkage. We illustrate and motivate our proposal through a real data example and simulations.