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There is growing interest in a data integration approach to survey sampling, particularly where population registers are linked for sampling and subsequent analysis. The reason for doing this is simple: it is only by linking the same individuals in the different sources that it becomes possible to create a data set suitable for analysis. But data linkage is not error free. Many linkages are nondeterministic, based on how likely a linking decision corresponds to a correct match, that is, it brings together the same individual in all sources. High quality linking will ensure that the probability of this happening is high. Analysis of the linked data should take account of this additional source of error when this is not the case. This is especially true for secondary analysis carried out without access to the linking information, that is, the often confidential data that agencies use in their record matching. We describe an inferential framework that allows for linkage errors when sampli ....

Computational Methods , Maximum Likelihood Methods Statistical Learning , Exploratory Methods , Data Sciences , Modeling Methods Statistical , Graphical Methods , Data Analysis , Exchangeable Linkage Error , Inite Population Inference , Linked Data , Robust Estimation ,