Statistical Data Integration in Survey Sampling: A Review Yang, Shu Kim, Jae Kwang Kim, Jae Kwang
dc.contributor.department Statistics 2020-10-07T20:51:44.000 2021-02-26T13:20:30Z 2021-02-26T13:20:30Z Wed Jan 01 00:00:00 UTC 2020 2020-01-01
dc.description.abstract <p>Finite population inference is a central goal in survey sampling. Probability sampling is the main statistical approach to finite population inference. Challenges arise due to high cost and increasing non-response rates. Data integration provides a timely solution by leveraging multiple data sources to provide more robust and efficient inference than using any single data source alone. The technique for data integration varies depending on types of samples and available information to be combined. This article provides a systematic review of data integration techniques for combining probability samples, probability and non-probability samples, and probability and big data samples. We discuss a wide range of integration methods such as generalized least squares, calibration weighting, inverse probability weighting, mass imputation and doubly robust methods. Finally, we highlight important questions for future research.</p>
dc.description.comments <p>This preprint is made available through arXiv: <a href="" target="_blank"></a>.</p>
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dc.identifier archive/
dc.identifier.articleid 1308
dc.identifier.contextkey 19705725
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/310
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 23:31:16 UTC 2022
dc.subject.disciplines Categorical Data Analysis
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Statistical Methodology
dc.subject.keywords Data fusion
dc.subject.keywords Generalizability
dc.subject.keywords Meta analysis
dc.subject.keywords Missingness at random
dc.subject.keywords Transportability
dc.title Statistical Data Integration in Survey Sampling: A Review
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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