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dc.contributor.authorChiang, Yuang-Chinen_US
dc.contributor.authorChen, Lin-Anen_US
dc.contributor.authorYang, Hsien-Chueh Peteren_US
dc.date.accessioned2014-12-08T15:16:00Z-
dc.date.available2014-12-08T15:16:00Z-
dc.date.issued2006-09-01en_US
dc.identifier.issn0266-4763en_US
dc.identifier.urihttp://dx.doi.org/10.1080/02664760600743464en_US
dc.identifier.urihttp://hdl.handle.net/11536/11892-
dc.description.abstractTo develop estimators with stronger efficiencies than the trimmed means which use the empirical quantile, Kim (1992) and Chen & Chiang (1996), implicitly or explicitly used the symmetric quantile, and thus introduced new trimmed means for location and linear regression models, respectively. This study further investigates the properties of the symmetric quantile and extends its application in several aspects. ( a) The symmetric quantile is more efficient than the empirical quantiles in asymptotic variances when quantile percentage a is either small or large. This reveals that for any proposal involving the alpha th quantile of small or large alpha s, the symmetric quantile is the right choice; (b) a trimmed mean based on it has asymptotic variance achieving a Cramer-Rao lower bound in one heavy tail distribution; ( c) an improvement of the quantiles-based control chart by Grimshaw & Alt ( 1997) is discussed; (d) Monte Carlo simulations of two new scale estimators based on symmetric quantiles also support this new quantile.en_US
dc.language.isoen_USen_US
dc.subjectregression quantileen_US
dc.subjectscale estimatoren_US
dc.subjecttrimmed meanen_US
dc.titleSymmetric quantiles and their applicationsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/02664760600743464en_US
dc.identifier.journalJOURNAL OF APPLIED STATISTICSen_US
dc.citation.volume33en_US
dc.citation.issue8en_US
dc.citation.spage807en_US
dc.citation.epage817en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000240434900004-
dc.citation.woscount2-
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