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dc.contributor.authorLu, HHSen_US
dc.contributor.authorHsieh, FSen_US
dc.date.accessioned2019-04-02T05:58:48Z-
dc.date.available2019-04-02T05:58:48Z-
dc.date.issued1997-10-01en_US
dc.identifier.issn1017-0405en_US
dc.identifier.urihttp://hdl.handle.net/11536/149684-
dc.description.abstractInterval scale grouped data have peculiar structures of their own rights among various archetypes of polytomous data that deserve special statistical treatments. Maximum likelihood type approaches along with heteroscedastic and transformation models are adapted to take into account this kind of architecture with current state-of-art computation capabilities. Meanwhile, misclassification rates instead of sum of squared residuals are suggested for model fitting and selection in light of the data formation. Successful applications of these methods are demonstrated by a set of empirical data regarding the endotracheal tube size selection for small children in the emergency room of a hospital.en_US
dc.language.isoen_USen_US
dc.subjectconstrained optimizationen_US
dc.subjectmaximum likelihood estimatorsen_US
dc.subjectmisclassification ratesen_US
dc.subjecttransformation modelsen_US
dc.titleTransformation models for interval scale grouped data with applicationsen_US
dc.typeArticleen_US
dc.identifier.journalSTATISTICA SINICAen_US
dc.citation.volume7en_US
dc.citation.spage841en_US
dc.citation.epage854en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:A1997YF24300002en_US
dc.citation.woscount0en_US
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