INTRODUCTION Matching has a long history of uses in statistical surveys and administrative data development. A business register consisting of names, addresses, and other identifying information such as total financial receipts might be constructed from tax and employment data bases (see chapters by Colledge, Nijhowne, and Archer). A survey of retail establishments or agricultural establishments might combine results from an area frame and a list frame. To produce a combined estimator, units from the area frame would need to be identified in the list frame (see Vogel-Kott chapter). To estimate the size of a (sub)population via capture-recapture techniques, one needs to accurately determine units common to two or more independent listings (Sekar and Deming 1949; Scheuren 1983; Winkler 1989b). Samples must be drawn appropriately to estimate overlap (Deming and Gleser 1959). Rather than develop a special survey to collect data for policy decisions, it might be more appropriate t

s Service. The study showed that the fewest errors typically occur at the beginning of a string and the error rates by character position increase monotonically as the position moves to the right. The enhancement basically consisted of adjusting the string comparator value upward by a fixed amount if the first four characters agreed; by lesser amounts if the first three, two, or one characters agreed. The string comparator examined by Budzkinsky (1991) consisted of the Jaro comparator with only the Winkler enhancement. The final enhancement due to Lynch and Winkler (1994) adjusts the string comparator value if the strings are longer than six characters and more than half the characters beyond the first four 4 agree. The final enhancement was based on detailed comparisons between versions of the comparator. The comparisons involved tens of thousands of pairs of last names, first names, and street names that did not agree on a character-by-character basis but were associated with truly...

Many applications of the Fellegi-Sunter model use simplifying assumptions and ad hoc modifications to improve matching efficacy. Because of model misspecification, distinctive approaches developed in one application typically cannot be used in other applications and do not always make use of advances in statistical and computational theory. An ExpectationMaximization (EMH) algorithm that constrains the estimates to a convex subregion of the parameter space is given. The EMH algorithm provides probability estimates that yield better decision rules than unconstrained estimates. The algorithm is related to results of Meng and Rubin (1993) on Multi-Cycle Expectation-Conditional Maximization algorithms and make use of results of Haberman (1977) that hold for large classes of loglinear models. Key Words: MCECM Algorithm, Latent Class, Computer Matching, Error Rate This paper provides a theory for obtaining constrained maximum likelihood estimates for latent-class, loglinear models on finite ...