Strong Convergence for Weighted Sums of WOD Random Variables and its Application in the EV Regression Model

被引：0
|
作者：
Ding, Liwang [1 ]
Jiang, Caoqing [2 ]
机构：
[1] Guangxi Univ Finance & Econ, Sch Math & Quantitat Econ, Mingxiu West Rd, Nanning, Guangxi, Peoples R China
[2] Guangxi Univ Finance & Econ, Sch Big Data & Artificial Intelligence, West Mingxiu Road, Nanning 530003, Guangxi, Peoples R China
来源：
APPLICATIONS OF MATHEMATICS | 2024年 / 69卷 / 01期
基金：
中国国家自然科学基金;
关键词：
errors-in-variables regression model; least squares estimator; widely orthant dependent; strong consistency; DEPENDENT RANDOM-VARIABLES; WEAK CONSISTENCY; PROBABILITY; ESTIMATORS; THEOREMS;
D O I：
10.21136/AM.2023.0004-23
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The strong convergence for weighted sums of widely orthant dependent (WOD) random variables is investigated. As an application, we further investigate the strong consistency of the least squares estimator in EV regression model for WOD random variables. A simulation study is carried out to confirm the theoretical results.
引用
收藏
页码：93 / 111
页数：19
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