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Adjusting misclassification using a second classifier with an external validation sample
The Journal of the Royal Statistical Society, Series A (Statistics in Society) ( IF 2 ) Pub Date : 2022-04-20 , DOI: 10.1111/rssa.12845
Jonas F. Schenkel 1 , Li‐Chun Zhang 1, 2, 3
Affiliation  

Administrative data may suffer from delays or mistakes in reporting. To adjust for the resulting measurement errors, it is often necessary to combine data from related sources, such as sample survey, administrative or ‘big’ data. However, the additional measure variable usually has a different definition and errors of its own, and the available joint data set may not have a completely known sampling distribution. We develop a modelling approach which capitalizes on one's knowledge and experience with the data source where they exist, and apply it to register- and survey-based Employed status. Comparisons are made to adjustments by hidden Markov models. Our approach is applicable to similar situations involving big data sources.

中文翻译:

使用带有外部验证样本的第二个分类器调整错误分类

行政数据可能会因报告延迟或错误而受到影响。为了调整由此产生的测量误差,通常需要结合来自相关来源的数据,例如抽样调查、行政或“大”数据。然而,附加测度变量通常具有不同的定义和自身的误差,并且可用的联合数据集可能不具有完全已知的抽样分布。我们开发了一种建模方法,利用一个人的知识和经验以及它们存在的数据源,并将其应用于基于登记和调查的就业状况。对隐马尔可夫模型的调整进行了比较。我们的方法适用于涉及大数据源的类似情况。
更新日期:2022-04-20
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