在無參數混合效應模型中進行由資料驅使的碎形波收縮

完整後設資料紀錄

DC 欄位	值	語言
dc.contributor.author	張慧雲	en_US
dc.contributor.author	Huei-yun Chang	en_US
dc.contributor.author	盧鴻興	en_US
dc.contributor.author	Henry Horng-Shing Lu	en_US
dc.date.accessioned	2014-12-12T02:20:13Z	-
dc.date.available	2014-12-12T02:20:13Z	-
dc.date.issued	1998	en_US
dc.identifier.uri	http://140.113.39.130/cdrfb3/record/nctu/#NT870337001	en_US
dc.identifier.uri	http://hdl.handle.net/11536/63989	-
dc.description.abstract	這篇論文利用觀察到的資料來選擇在無參數混合效應模型中的碎型波收縮的參數,方法包括交叉確認法(cross-validation method)與廣義的交叉確認法(generalized cross-validation)兩類方法.經由模擬研究,我們發現,特別是在樣本數較小及誤差程度不知道時,廣義的交叉確認法是較有效的,較可靠的,及較快的方法.	zh_TW
dc.description.abstract	This study investigates the data-driven selection method of the threshold parameter in the wavelet shrinkage method for nonparametric mixed-effects models. The cross-validation and generalized cross-validation methods are studied. Through the simulation studies, the generalized cross-validation method turns out to be a efficient, reliable, and fast method in varticular when sample sizes are small and the noise levels are unknown.	en_US
dc.language.iso	en_US	en_US
dc.subject	無參數混合效應模型	zh_TW
dc.subject	碎形波收縮	zh_TW
dc.subject	無參數迴歸	zh_TW
dc.subject	最佳線性不偏預測	zh_TW
dc.subject	交叉確認法	zh_TW
dc.subject	廣義交叉確認法	zh_TW
dc.subject	Nonparametric Mixed-effects Models	en_US
dc.subject	Wavelet Shrinkage	en_US
dc.subject	Nonparametric Regression	en_US
dc.subject	best linear unbiased prediction(BLUP)	en_US
dc.subject	cross-validation method	en_US
dc.subject	generalized cross-validation method	en_US
dc.title	在無參數混合效應模型中進行由資料驅使的碎形波收縮	zh_TW
dc.title	Data-Driven Wavlet Shrinkage for Nonparametric Mixed-effects Models	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