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dc.contributor.authorLi, Wenjun
dc.contributor.authorStanek, Edward J. III
dc.contributor.authorSinger, Julio M.
dc.date2022-08-11T08:10:21.000
dc.date.accessioned2022-08-23T17:05:28Z
dc.date.available2022-08-23T17:05:28Z
dc.date.issued2012-01-01
dc.date.submitted2014-06-03
dc.identifier.citationLi W, Stanek EJ 3rd, Singer JM. Design-based random permutation models with auxiliary information(¶). Statistics (Ber). 2012 Jan 1;46(5):663-671. <a href="http://dx.doi.org/10.1080/02331888.2010.545408" target="_blank">Link to article on publisher's site</a>
dc.identifier.issn233-1888 (Linking)
dc.identifier.doi10.1080/02331888.2010.545408
dc.identifier.pmid23645951
dc.identifier.urihttp://hdl.handle.net/20.500.14038/44864
dc.description.abstractWe extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=23645951&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640589/pdf/nihms263341.pdf
dc.subjectauxiliary variable
dc.subjectdesign-based inference
dc.subjectprediction
dc.subjectfinite sampling
dc.subjectrandom permutation model
dc.subjectsimultaneous permutation
dc.subjectStatistical Models
dc.titleDesign-based random permutation models with auxiliary information
dc.typeJournal Article
dc.source.journaltitleStatistics
dc.source.volume46
dc.source.issue5
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/prevbeh_pp/286
dc.identifier.contextkey5647113
html.description.abstract<p>We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.</p>
dc.identifier.submissionpathprevbeh_pp/286
dc.contributor.departmentDepartment of Medicine, Division of Preventive and Behavioral Medicine
dc.source.pages663-671


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