ABSTRACT A recently proposed procedure for correcting inconsistent (i.e. incoherent) probability assessments is specifically tailored for the statistical matching problem with misclassification component. Such procedure is based on distance minimization encoded in mixed integer programming (MIP) problems and it results particularly apt to deal with assessments stemming from different sources of information. The statistical matching problem is one of those cases. The statistical matching problem has been recently studied also inside a misclassification setting. To proceed with a correction in such a framework, if marginal assessments on the conditioning event are wanted to remain fixed, the only possible solutions are the closest Fréchet–Hoeffding bounds for the misclassification probabilities. On the contrary, if also the marginal probabilities are allowed to be modified, the -based procedure can be applied by a straightforward translation in an MIP problem. Such procedure is applied to a healthcare expenditures and health conditions data example.

中文翻译：

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统计匹配中错误分类的概率不一致校正，以医疗保健为例

摘要 最近提出的用于纠正不一致（即不连贯）概率评估的程序是专门为具有错误分类组件的统计匹配问题量身定制的。这种程序基于在混合整数规划 (MIP) 问题中编码的距离最小化，它的结果特别适合处理源自不同信息源的评估。统计匹配问题就是其中一种情况。最近也在错误分类设置中研究了统计匹配问题。为了在这样的框架中进行更正，如果希望对条件事件的边际评估保持固定，唯一可能的解决方案是错误分类概率的最接近的 Fréchet-Hoeffding 界限。相反，如果还允许修改边际概率，则可以通过在 MIP 问题中直接转换来应用基于 - 的过程。这种程序被应用于医疗保健支出和健康状况数据示例。