文章基本信息

标题：Bias Correction in Clustered Underreported Data
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作者：Guilherme Lopes de Oliveira ; Raffaele Argiento ; Rosangela Helena Loschi 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2022
卷号：17
期号：1
页码：95-126
DOI：10.1214/20-BA1244
语种：English
出版社：International Society for Bayesian Analysis
摘要：Data quality from poor and socially deprived regions have given rise to many statistical challenges. One of them is the underreporting of vital events leading to biased estimates for the associated risks. To deal with underreported count data, models based on compound Poisson distributions have been commonly assumed. To be identifiable, such models usually require extra and strong information about the probability of reporting the event in all areas of interest, which is not always available. We introduce a novel approach for the compound Poisson model assuming that the areas are clustered according to their data quality. We leverage these clusters to create a hierarchical structure in which the reporting probabilities decrease as we move from the best group to the worst ones. We obtain constraints for model identifiability and prove that only prior information about the reporting probability in areas experiencing the best data quality is required. Several approaches to model the uncertainty about the reporting probabilities are presented, including reference priors. Different features regarding the proposed methodology are studied through simulation. We apply our model to map the early neonatal mortality risks in Minas Gerais, a Brazilian state that presents heterogeneous characteristics and a relevant socio-economical inequality.
关键词：62F15;62J12;compound Poisson model;generalized Beta distribution;Jeffreys prior;model identifiability;neonatal mortality;underreporting