Reducing Variance in Univariate Smoothing
學年 95
學期 2
出版（發表）日期 2007-04-15
作品名稱 Reducing Variance in Univariate Smoothing
作品名稱（其他語言）
著者 Ming-Yen Cheng; Liang Peng; Jyh-Shyang Wu
單位
出版者
著錄名稱、卷期、頁數 The Annals of Statistics 35(2), pp.522-542
摘要 A variance reduction technique in nonparametric smoothing is proposed: at each point of estimation, form a linear combination of a preliminary estimator evaluated at nearby points with the coefficients specified so that the asymptotic bias remains unchanged. The nearby points are chosen to maximize the variance reduction. We study in detail the case of univariate local linear regression. While the new estimator retains many advantages of the local linear estimator, it has appealing asymptotic relative efficiencies. Bandwidth selection rules are available by a simple constant factor adjustment of those for local linear estimation. A simulation study indicates that the finite sample relative efficiency often matches the asymptotic relative efficiency for moderate sample sizes. This technique is very general and has a wide range of applications.
關鍵字 Bandwidth;coverage probability;kernel;local linear regression;nonparametric smoothing;variance reduction
語言 en_US
ISSN 0090-5364
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/110301 )