Measurement error in multinomial data is a well-known and well-studied inferential problem that is encountered in many fields, including engineering, biomedical and omics research, ecology, finance, official statistics, and social sciences. Methods developed to accommodate measurement error in multinomial data are typically equipped to handle false negatives or false positives, but not both. We provide a unified framework for accommodating both forms of measurement error using a Bayesian hierarchical approach. We demonstrate the proposed method's performance on simulated data and apply it to acoustic bat monitoring and official crime data.

翻译：多类别数据中的测量误差是一个广为人知且被深入研究的推断问题，常见于工程学、生物医学与组学研究、生态学、金融学、官方统计及社会科学等多个领域。现有用于处理多类别数据测量误差的方法通常只能应对假阴性或假阳性误差，而无法同时处理两者。本文提出一个统一的贝叶斯分层框架，可同时处理这两种形式的测量误差。我们通过模拟数据验证了所提方法的性能，并将其应用于蝙蝠声学监测与官方犯罪数据分析。