線性迴歸模型及聯立方程式模型之Mallows形式有界迴歸分位向量

完整後設資料紀錄

DC 欄位	值	語言
dc.contributor.author	莊弘昌	en_US
dc.contributor.author	Chuang, Hung-Chang	en_US
dc.contributor.author	陳鄰安	en_US
dc.contributor.author	Chen Lin-An	en_US
dc.date.accessioned	2014-12-12T02:17:11Z	-
dc.date.available	2014-12-12T02:17:11Z	-
dc.date.issued	1996	en_US
dc.identifier.uri	http://140.113.39.130/cdrfb3/record/nctu/#NT850337007	en_US
dc.identifier.uri	http://hdl.handle.net/11536/61733	-
dc.description.abstract	我們導出線性迴歸模型及聯立方程式模型的Mallows形式有界迴歸分位向量之近似分配。我們也做蒙地卡羅(Monte Carlo)模擬，並比較均方誤差且驗證出如果獨立變數產生毛差(gross errors)時，則有界迴歸分位向量將比無界迴歸分位向量(見Koenker及Bassett(1978))來得有效率。 We present asymptotic distributions of the Mallows type bounded-influencereg ression quantile for the linear regression model and also the simultaneousequa tions model. Monte Carlo simulation comparing mean squared errors showsthat th e bounded-influence one is more efficient than the unbounded- influence one(Koe nker and Bassett(1978))when gross errors occur in the independent-variables-s pace.	zh_TW
dc.language.iso	zh_TW	en_US
dc.subject	影響	zh_TW
dc.subject	迴歸分位向量	zh_TW
dc.subject	influence	en_US
dc.subject	regression quantile	en_US
dc.title	線性迴歸模型及聯立方程式模型之Mallows形式有界迴歸分位向量	zh_TW
dc.title	Mallows Type Bounded Influence Regression Quantile for Linear Regression Model and Simultaneous Equations Model	en_US
dc.type	Thesis	en_US
dc.contributor.department	統計學研究所	zh_TW
顯示於類別：	畢業論文