A Practical Approach to Proper Inference with Linked Data.
Entity resolution (ER), comprising record linkage and deduplication, is the process of merging noisy databases in the absence of unique identifiers to remove duplicate entities. One major challenge of analysis with linked data is identifying a representative record among determined matches to pass t...
| Publicado en: | American Statistician Vol. 76; no. 4; pp. 384 - 394 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Nov2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=160099133&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160099133 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Nov2022 vid: 76 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 160099133 10.1080/00031305.2022.2041482 ppf: 384 ppct: 10 formats: tig: atl: A Practical Approach to Proper Inference with Linked Data. aug: au: Kaplan, Andee Betancourt, Brenda Steorts, Rebecca C. affil: Department of Statistics, Colorado State University, Fort Collins, CO Department of Statistics, University of Florida, Gainesville, FL Departments of Statistical Science and Computer Science, Duke University, Durham, NC su: North Carolina Political affiliation Election boards Voter registration Task analysis Logistic regression analysis sug: subj: Political affiliation North Carolina Political Organizations Other General Government Support Election boards Voter registration Task analysis Logistic regression analysis keyword: Bayesian methods Canonicalization Entity resolution Error propagation Record linkage Bayesian methods Canonicalization Entity resolution Error propagation Record linkage ab: Entity resolution (ER), comprising record linkage and deduplication, is the process of merging noisy databases in the absence of unique identifiers to remove duplicate entities. One major challenge of analysis with linked data is identifying a representative record among determined matches to pass to an inferential or predictive task, referred to as the downstream task. Additionally, incorporating uncertainty from ER in the downstream task is critical to ensure proper inference. To bridge the gap between ER and the downstream task in an analysis pipeline, we propose five methods to choose a representative (or canonical) record from linked data, referred to as canonicalization. Our methods are scalable in the number of records, appropriate in general data scenarios, and provide natural error propagation via a Bayesian canonicalization stage. The proposed methodology is evaluated on three simulated datasets and one application – determining the relationship between demographic information and party affiliation in voter registration data from the North Carolina State Board of Elections. We first perform Bayesian ER and evaluate our proposed methods for canonicalization before considering the downstream tasks of linear and logistic regression. Bayesian canonicalization methods are empirically shown to improve downstream inference in both settings through prediction and coverage. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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