Maximum Likelihood Estimation for Contingency Tables and Logistic Regression with Incorrectly Linked Data
Date
2011
Authors
Chipperfield, James O.
Bishop, Glenys
Campbell, Paul D.
Journal Title
Journal ISSN
Volume Title
Publisher
Statistics Canada
Abstract
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.
Description
Keywords
Keywords: Contingency tables; Data linkage; Logistic regression; Maximum likelihood; Probabilistic linkage
Citation
Collections
Source
Survey Methodology
Type
Journal article
Book Title
Entity type
Access Statement
Open Access