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dc.contributor.author陳韻如en_US
dc.contributor.authorChen, Yun-Ruen_US
dc.contributor.author陳鄰安en_US
dc.contributor.authorChen Lin-Anen_US
dc.date.accessioned2014-12-12T02:18:33Z-
dc.date.available2014-12-12T02:18:33Z-
dc.date.issued1997en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT860338010en_US
dc.identifier.urihttp://hdl.handle.net/11536/62705-
dc.description.abstractWith the assumption of known the center of the distribution of a random variable, we study the estimation of population quantile and a trimmed population variance. For estimating the population quantile function for the symmetric location model, we show that the sample symmetric quantile is asymptotically more efficient in the sense of smaller asymptotic variance and then shorter confidence interval than those by the ordinary sample quantile. For estimation of the trimmed variance, we compare the sample trimmed variance constructed by ordinary quantiles and the symmetric type sample trimmed variance. Although these two estimators are asymptotically equivalent in distribution, however, a small sample Monte Carlo comparison shows that the latter one is relatively more efficient than the former one in terms of mean squares errors.zh_TW
dc.language.isozh_TWen_US
dc.subject分位數zh_TW
dc.subject對稱型分位數zh_TW
dc.subject截斷變異數zh_TW
dc.subject變異數zh_TW
dc.subjectQuantileen_US
dc.subjectSymmetric Quantileen_US
dc.subjectTrimmed Varianceen_US
dc.subjectVarianceen_US
dc.title無母數分配中心點已知條件下之對稱型分位數及截斷變異數zh_TW
dc.titleEstiamtion of Quantile and Trimmed Variance by the Symmetric Quantile When the Center of Distribution is knownen_US
dc.typeThesisen_US
dc.contributor.department統計學研究所zh_TW
Appears in Collections:Thesis