Mass imputation for two-phase sampling Park, Seho Kim, Jae Kwang Kim, Jae Kwang
dc.contributor.department Statistics 2019-09-21T12:20:16.000 2020-07-02T06:57:29Z 2020-07-02T06:57:29Z Tue Jan 01 00:00:00 UTC 2019 2020-04-03 2019-04-03
dc.description.abstract <p>Two-phase sampling is a cost-effective method of data collection using outcomedependent sampling for the second-phase sample. In order to make efficient use of auxiliary information and to improve domain estimation, mass imputation can be used in two-phase sampling. Rao and Sitter (1995) introduce mass imputation for two-phase sampling and its variance estimation under simple random sampling in both phases. In this paper, we extend the Rao–Sitter method to general sampling design. The proposed method is further extended to mass imputation for categorical data. A limited simulation study is performed to examine the performance of the proposed methods.</p>
dc.description.comments <p>This is a manuscript of an article published as S. Park and J.K. Kim (2019). "Mass imputation for two-phase sampling", <em>Journal of the Korean Statistical Society. doi: </em><a href="" target="_blank" title="Persistent link using digital object identifier">10.1016/j.jkss.2019.03.002</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1258
dc.identifier.contextkey 15137343
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/256
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 22:59:00 UTC 2022
dc.source.uri 10.1016/j.jkss.2019.03.002
dc.subject.disciplines Categorical Data Analysis
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Auxiliary information
dc.subject.keywords Categorical data
dc.subject.keywords Domain estimation
dc.subject.keywords Outcome-dependent sampling
dc.title Mass imputation for two-phase sampling
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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