Statistical matching using fractional imputation

dc.contributor.author Kim, Jae Kwang
dc.contributor.author Kim, Jae Kwang
dc.contributor.author Berg, Emily
dc.contributor.author Park, Taesung
dc.contributor.department Statistics
dc.date 2018-02-18T16:50:54.000
dc.date.accessioned 2020-07-02T06:56:39Z
dc.date.available 2020-07-02T06:56:39Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.issued 2016-06-01
dc.description.abstract <p>Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent expectation-maximisation (EM) sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.</p>
dc.description.comments <p>This article is published as J.K. Kim, E, Berg, and T. Park. (2016). “Statistical matching using fractional imputation”. <em>Survey Methodology</em>, 42, 19–40. Published with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/121/
dc.identifier.articleid 1099
dc.identifier.contextkey 10453788
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/121
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90423
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/121/2016_Kim_StatisticalMatching.pdf|||Fri Jan 14 19:12:53 UTC 2022
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Data combination
dc.subject.keywords Data fusion
dc.subject.keywords Hot deck imputation
dc.subject.keywords Split questionnaire design
dc.subject.keywords Measurement error model
dc.title Statistical matching using fractional imputation
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication fdf914ae-e48d-4f4e-bfa2-df7a755320f4
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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