CONSISTENCY OF ESTIMATOR FOR NONPARAMETRIC REGRESSION UNDER NEGATIVELY SUPERADDITIVE DEPENDENT RANDOM VARIABLES

被引：0

作者：

Ding, Liwang ^{[1
]}

Jiang, Caoqing ^{[2
]}

机构：

[1] Guangxi Univ Finance & Econ, Sch Math & Quantitat Econ, Nanning 530003, Peoples R China

[2] Guangxi Univ Finance & Econ, Sch Big Data & Artificial Intelligence, Nanning 530003, Peoples R China

来源：

JOURNAL OF MATHEMATICAL INEQUALITIES | 2023年 / 17卷 / 04期

基金：

中国国家自然科学基金;

关键词：

and phrases; Negatively superadditive dependent random variables; wavelet estimators; complete consistency; strong consistency; COMPLETE CONVERGENCE; WAVELET ESTIMATOR; WEIGHTED SUMS; MODEL; SURE;

D O I：

10.7153/jmi-2023-17-82

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this article, we discuss the complete consistency and strong consistency of wavelet estimators in nonparametric regression model with negatively superadditive dependent random variables, which improve and extend some existing ones. Finally, some numerical simulations are carried out to confirm the theoretical results.

引用

页码：1259 / 1274

页数：16

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