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Maximum likelihood estimation for contingency tables and logistic regression with incorrectly linked data

机译：列联表的最大似然估计和错误链接数据的逻辑回归

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摘要

Data linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. It is a very common way to enhance dimensions such as time and breadth or depth of detail. Data linkage is often not an error-free process and can lead to linking a pair of records that do not belong to the same unit. There is an explosion of record linkage applications, yet there has been little work on assuring the quality of analyses using such linked files. Naively treating such a linked file as if it were linked without errors will, in general, lead to biased estimates. This paper develops a maximum likelihood estimator for contingency tables and logistic regression with incorrectly linked records. The estimation technique is simple and is implemented using the well-known EM algorithm. A well known method of linking records in the present context is probabilistic data linking. The paper demonstrates the effectiveness of the proposed estimators in an empirical study which uses probabilistic data linkage.
机译：数据链接是将两个或多个文件中被认为属于同一单位（例如，个人或业务）的记录汇总在一起的行为。这是增加尺寸（例如时间和宽度或深度）的一种非常常见的方法。数据链接通常不是一个没有错误的过程，并且可能导致链接一对不属于同一单元的记录。记录链接应用程序激增，但在确保使用此类链接文件进行分析的质量方面，开展的工作很少。通常，将这样一个链接的文件当作链接正确无误地对待，通常会导致估计偏差。本文针对联结表和不正确链接记录的逻辑回归开发了最大似然估计。估计技术很简单，并且使用众所周知的EM算法实现。在当前上下文中链接记录的一种众所周知的方法是概率数据链接。本文在使用概率数据链接的实证研究中证明了拟议的估计量的有效性。

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