IMPROVED DECISION RULES IN THE FELLEGI-SUNTER MODEL OF RECORD LINKAGE

Many applications of the FeUegi-Sunter model use simplifying assumptions and ad hoe modifications to improve matching efficacy. Because of model misspecification, distinctive approaches developed in one application typicaUy cannot be used in other applications and do not always make use of advances in statistical and computational theory. This paper gives an ExpectationMaximization 0EMH) algorithm that constrains the estimates to a convex subregion of the parameter space. The EMH algorithm provides probability estimates that yield better decision rules than unconstrained estimates. The algorithm is related to the Multi-Cycle ExpectationConditional Maximization algorithm (Meng and Rubin 1993) and is deduced via results of Haberman (1977) that hold for large classes of loglinear models.