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Consider two files of records. Within each file, each record corresponds to a different population unit; but the two files correspond to the same general population. We want to identify "matches” i.e., pairs of records (from the two files) that each correspond to the same population unit.
Each record contains data in K fields which correspond to characteristics such as age, race, etc. We may observe patterns of agreement/disagreement among the fields, for each pair of records. Using this information, we want as best as possible to identify matches. The problem of how best to use the field information has been addressed for K=3, under assumption that the events "agreement in field i," i=1, ..., K are stochastically mutually independent -- for true matches and likewise for true nonmatches. We address the problem for K>3, and avoid reliance on the assumption of independence by fitting interaction terms which reflect stochastic positive dependences.
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