Record linkage aims to bring records together from two or more files that belong to the same statistical entity. Linkage errors can occur during this process. Ignoring these linkage errors can lead...Show moreRecord linkage aims to bring records together from two or more files that belong to the same statistical entity. Linkage errors can occur during this process. Ignoring these linkage errors can lead to biased inference. There is a growing emphasis on accounting for linkage errors in the statistical analysis of categorical data and contingency tables. In this thesis, we developed three new approaches for compensating for linkage errors in contingency tables. The first approach, the regularised estimator, uses ideas from the application of regularisation of ill-conditioned matrices. Two other approaches use probabilities to compute the expected contingency table given the observed contingency table and to weight three existing correction methods with their estimated mean square error. The new approaches were tested together with two existing estimators by means of a simulation study. For dependent contingency tables, we propose to use the expected value approach with a prior distribution that uses information about the observed values of the contingency table. Moreover, we propose to use the existing Q approach for independent contingency tables. The regularised estimator seems to have a lot of potential for both dependent and independent tables, but improvement is still needed.Show less
