Wavelet Adaptive Pointwise Density Estimations with Super-smooth Noises [带超级光滑噪声密度函数的小波自适应点态估计] - 文章详情页

作者：

Wu, C. (Wu, C..) | Zeng, X.C. (Zeng, X.C..) | Wang, J.R. (Wang, J.R..)

收录：

Scopus PKU CSCD

摘要：

This　paper　considers　pointwise　deconvolution　estimation　of　density　functions　under　the　local　Hölder　condition　by　wavelet　method.　We　firstly　give　a　lower　bound　of　any　estimator　with　super-smooth　noises.　Then　the　practical　linear　wavelet　estimator　is　constructed　to　obtain　the　optimal　convergence　rate,　which　means　that　the　rate　coincides　with　the　lower　bound.　The　strong　convergence　rate　of　the　defined　wavelet　estimator　is　also　provided.　It　should　be　pointed　out　that　all　above　estimations　are　adaptive.　©　2019,　Chinese　Academy　of　Sciences.　All　right　reserved.

关键词：

Adaptivity; Optimality; Pointwise density estimation; Wavelets

作者机构：

[ 1 ] [Wu, C.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Zeng, X.C.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Wang, J.R.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China

通讯作者信息：

[Zeng, X.C.]College of Applied Sciences, Beijing University of TechnologyChina

电子邮件地址：

zengxiaochen@bjut.edu.cn

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