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Matching and Record Linkage
 Business Survey Methods
, 1995
"... 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 b ..."
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Cited by 117 (15 self)
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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 VogelKott chapter). To estimate the size of a (sub)population via capturerecapture 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
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 37 (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 27 (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...