Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/99155
DC FieldValueLanguage
dc.contributor.authorChang-Yun Linzh_TW
dc.contributor.author林長鋆zh_TW
dc.date2018-02-01-
dc.date.accessioned2019-10-21T01:33:18Z-
dc.date.available2019-10-21T01:33:18Z-
dc.identifier.urihttp://hdl.handle.net/11455/99155-
dc.description.abstractMany existing methods for constructing optimal split-plot designs, such as D-optimal or A-optimal designs, focus only on minimizing the variance of the parameter estimates for the fitted model. However, the true model is usually more complicated; hence, the fitted model is often misspecified. If significant effects not included in the model exist, then the estimates could be highly biased. Therefore a good split-plot design should be able to simultaneously control the variance and the bias of the estimates. In this article, I propose a new method for constructing optimal split-plot designs that are robust under model misspecification. Four examples are provided to demonstrate that my method can produce efficient split-plot designs with smaller overall aliasing. Simulation studies are performed to verify that the robust designs I construct have high power, low false discovery rate, and small mean squared error.zh_TW
dc.language.isoen_USzh_TW
dc.publisherJournal of Quality Technologyzh_TW
dc.relationJOURNAL OF QUALITY TECHNOLOGY 2018, VOL. 50, NO. 1, 76–87zh_TW
dc.relation.urihttps://www.tandfonline.com/eprint/hJCfVXj2muyVu2gx7yqz/fullzh_TW
dc.subjectA-efficiencyzh_TW
dc.subjectcoordinate-exchange algorithmzh_TW
dc.subjectD-efficiencyzh_TW
dc.subjectgeneralized least squareszh_TW
dc.subjectloss functionzh_TW
dc.subjectmean squared errorzh_TW
dc.titleRobust split-plot designs for model misspecificationzh_TW
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1080/00224065.2018.1404325zh_TW
dc.awards2018zh_TW
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1en_US-
item.fulltextwith fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
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