This paper provides an overview of methods and systems developed for record linkage. Modern record linkage begins with the pioneering work of Newcombe and is especially based on the formal mathematical model of Fellegi and Sunter. In their seminal work, Fellegi and Sunter introduced many powerful ideas for estimating record linkage parameters and other ideas that still influence record linkage today. Record linkage research is characterized by its synergism of statistics, computer science, and operations research. Many difficult algorithms have been developed and put in software systems. Record linkage practice is still very limited. Some limits are due to existing software. Other limits are due to the difficulty in automatically estimating matching parameters and error rates, with current research highlighted by the work of Larsen and Rubin. Keywords: computer matching, modeling, iterative fitting, string comparison, optimization RsSUMs Cet article donne une vue d'ensemble sur les ...

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...

This paper provides background on record linkage methods that can be used in combining data from a variety of sources such as person lists business lists. It also gives some areas of current research.

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 ...

Although terminology differs, there is considerable overlap between record linkage methods based on the Fellegi-Sunter model (JASA 1969) and Bayesian networks used in machine learning (Mitchell 1997). Both are based on formal probabilistic models that can be shown to be equivalent in many situations (Winkler 2000). When no missing data are present in identifying fields and training data are available, then both can efficiently estimate parameters of interest. When missing data are present, the EM algorithm can be used for parameter estimation in Bayesian Networks when there are training data (Friedman 1997) and in record linkage when there are no training data (unsupervised learning). EM and MCMC methods can be used for automatically estimating error rates in some of the record linkage situations (Belin and Rubin

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 Fellegi-Sunter (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...

This paper provides a survey of two classes of methods that can be used in determining and improving the quality of individual files or groups of files. The first are edit/imputation methods for maintaining business rules and for imputing for missing data. The second are methods of data cleaning for finding duplicates within files or across files. Published by Elsevier Ltd.

Data Cleaning methods are used for finding duplicates within a file or across sets of files. This overview provides background on the Fellegi-Sunter model of record linkage. The Fellegi-Sunter model provides an optimal theoretical classification rule. Fellegi and Sunter introduced methods for automatically estimating optimal parameters without training data that we extend to many real world situations. Keywords EM Algorithm, string comparator, unsupervised learning. 1.

Bureau of the Census This paper extends techniques for frequency-based matching (see e.g., Fellegi and Sunter 1969). The extended techniques allow table-building under weaker assumptions than those typically used in practice. Although CPU requirements can increase, human intervention can be reduced in some situations.