The mean consistency of wavelet estimators for convolutions of the density functions

被引：1

作者：

Guo, Huijun ^{[1
,2
]}

Wang, Jinru ^{[1
]}

Tian, Xinyan ^{[1
]}

机构：

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

[2] Guilin Univ Elect Technol, Sch Math & Comp Sci, Guilin 541004, Guangxi, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2018年 / 343卷

基金：

中国国家自然科学基金;

关键词：

Density convolution; Wavelet; L-p-consistency; Noise; DECONVOLUTION;

D O I：

10.1016/j.cam.2018.04.045

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In practical applications, people sometimes do not know whether the estimated function is smooth, and it is reasonable to consider the consistency of an estimator. Furthermore, the acquired data are usually contaminated by various random noises. In this paper, we develop the wavelet estimators for m-fold convolutions of the unknown density functions and consider their Lp (1 <= p <= infinity) consistency under noiseless and additive noise situations, respectively. Finally, simulation studies illustrate the good performances of our nonparametric wavelet estimators. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：1 / 11

页数：11

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