AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home AIMS Mathematics Article
PDF (244.9 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Data-driven wavelet estimations in the convolution structure density model

School of Mathematics and Statistics, Weifang University, Weifang 261061, China
Show Author Information

Abstract

Based on a data-driven kernel estimator, Lepski and Willer considered the problem of adaptive L p risk estimations in the convolution structure density model in 2017 and 2019. This current paper studies the same problem with a data-driven wavelet estimator on Besov spaces, as wavelet estimations offer fast algorithm and provide more local information. Our results can reduce to the traditional adaptive wavelet estimations in the classical density model with no errors, as well as deconvolutional model.

Keywords

waveletsBesov spacesdensity estimationsgeneralized deconvolution modeladaptivity
CLC number: 42C40, 62G07, 62G20

References

【1】
【1】
 
 
Crossref Google Scholar
AIMS Mathematics
Volume 9 Issue 7,
July 2024
Pages 17076-17088
DOI: 10.3934/math.2024829

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Cite this Report
Close
Close
Cite this article:
Cao K. Data-driven wavelet estimations in the convolution structure density model. AIMS Mathematics, 2024, 9(7): 17076-17088. https://doi.org/10.3934/math.2024829

131

Views

2

Downloads

0

Crossref

0

Web of Science

0

Scopus
Received: 13 March 2024
Revised: 06 May 2024
Accepted: 08 May 2024
Published: 15 July 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)