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Minimum Bias Design for Polynomial Regression

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dc.contributor.authorJang, Dae-Heung-
dc.contributor.authorKim, Youngil-
dc.date.accessioned2023-03-08T18:22:26Z-
dc.date.available2023-03-08T18:22:26Z-
dc.date.issued2015-12-
dc.identifier.issn1225-066X-
dc.identifier.issn2383-5818-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/64420-
dc.description.abstractTraditional criteria for optimum experimental designs depend on the specifications of the model; however, there will be a dilemma when we do not have perfect knowledge about the model. Box and Draper (1959) suggested one direction to minimize bias that may occur in this situation. We will demonstrate some examples with exact solutions that provide a no-bias design for polynomial regression. The most interesting finding is that a design that requires less bias should allocate design points away from the border of the design space.-
dc.format.extent8-
dc.language한국어-
dc.language.isoKOR-
dc.publisherKOREAN STATISTICAL SOC-
dc.titleMinimum Bias Design for Polynomial Regression-
dc.typeArticle-
dc.identifier.doi10.5351/KJAS.2015.28.6.1227-
dc.identifier.bibliographicCitationKOREAN JOURNAL OF APPLIED STATISTICS, v.28, no.6, pp 1227 - 1234-
dc.identifier.kciidART002068178-
dc.description.isOpenAccessN-
dc.citation.endPage1234-
dc.citation.number6-
dc.citation.startPage1227-
dc.citation.titleKOREAN JOURNAL OF APPLIED STATISTICS-
dc.citation.volume28-
dc.type.docTypeArticle-
dc.publisher.location대한민국-
dc.subject.keywordAuthorbias-
dc.subject.keywordAuthorminimum bias design-
dc.subject.keywordAuthorQ-optimal design-
dc.subject.keywordAuthorintegrated mean squared error(IMSE)-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.description.journalRegisteredClasskci-
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