Asymptotic normality for wavelet deconvolution density estimators

被引：14

作者：

Liu, Youming ^{[1
]}

Zeng, Xiaochen ^{[1
]}

机构：

[1] Beijing Univ Technol, Dept Appl Math, Beijing 100124, Peoples R China

来源：

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS | 2020年 / 48卷 / 01期

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

Wavelet estimator; Central limit theorem; Deconvolution; Density function; CENTRAL-LIMIT-THEOREM; LP-NORMS; ERROR;

D O I：

10.1016/j.acha.2018.05.006

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This current paper shows the asymptotic normality for wavelet deconvolution density estimators, when a density function belongs to some L-P(R) (p > 2) and the noises are moderately ill-posed with the index beta. The estimators include both the linear and non-linear wavelet ones. It turns out that the situation for 0 < beta <= 1 is more complicated than that for beta > 1. (C) 2018 Elsevier Inc. All rights reserved.

引用

页码：321 / 342

页数：22

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