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Using the EM Algorithm for Weight Computation in the FellegiSunter Model of Record Linkage
 Proceedings of the Section on Survey Research Methods, American Statistical Association
, 2000
"... Let AxB be the product space of two sets A and B which is divided into a (pairs representing the same entity) and nonmatches (pairs representing different entities). Linkage rules are those that divide AxB into links (designated matches), possible links (pairs for which we delay a decision), and non ..."
Abstract

Cited by 45 (15 self)
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Let AxB be the product space of two sets A and B which is divided into a (pairs representing the same entity) and nonmatches (pairs representing different entities). Linkage rules are those that divide AxB into links (designated matches), possible links (pairs for which we delay a decision), and nonlinks (designated nonmatches). Under fixed bounds on the error rates, Fellegi and Sunter (1969) provided a linkage rule that is optimal in the sense that it minimizes the set of possible links. The optimality is dependent on knowledge of certain joint inclusion probabilities that are used in a crucial likelihood ratio. In applying the record linkage model, assumptions are often made that allow estimation of weights that are a function of the joint inclusion probabilities. If the assumptions are not met, then the linkage procedure using estimates computed under the assumptions may not be optimal. This paper describes a method for estimating weights using the EM Algorithm under less restrictive assumptions. The weight computation automatically incorporates a Bayesian adjustment based on file characteristics.
Improved decision rules in the FellegiSunter model of record linkage
 in American Statistical Association Proceedings of Survey Research Methods Section
, 1993
"... Many applications of the FellegiSunter 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 advance ..."
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Cited by 33 (12 self)
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Many applications of the FellegiSunter 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 MultiCycle ExpectationConditional 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 latentclass, loglinear models on finite state spaces. The work is related to ExpectationMaximization (EM) algorithms by Meng and Rubin (1993) for obtaining unconstrained maximum likelihood estimates. Meng and Rubin generalized the original ideas of Dempster,
An Application Of The FellegiSunter Model Of Record Linkage To The 1990 U.S. Decennial Census
 U.S. Decennial Censusâ€ť. Technical report, US Bureau of the Census
, 1987
"... This paper describes a methodology for computer matching the Post Enumeration Survey with the Census. Computer matching is the first stage of a process for producing adjusted Census counts. All crucial matching parameters are computed solely using characteristics of the files being matched. No a pri ..."
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Cited by 25 (4 self)
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This paper describes a methodology for computer matching the Post Enumeration Survey with the Census. Computer matching is the first stage of a process for producing adjusted Census counts. All crucial matching parameters are computed solely using characteristics of the files being matched. No a priori knowledge of truth of matches is assumed. No previously created lookup tables are needed. The methods are illustrated with numerical results using files from the 1988 Dress Rehearsal Census for which the truth of matches is known. Key words and phrases. EM Algorithm; String Comparator Metric; LP Algorithm; Decision Rule; Error Rate. 1. INTRODUCTION This paper describes a particular application of the FellegiSunter (1969) model of record linkage. New computational methods are used for computer matching the Post Enumeration Survey (PES) with the Census. The PES is used to produce adjusted Census counts. Computer matching is the first stage of PES processing. All crucial matching paramete...