The rates of strong consistency for estimators in heteroscedastic partially linear errors-in-variables model for widely orthant dependent samples

被引：0
|
作者：
Wu, Yi [1 ,2 ]
Wang, Xuejun [1 ]
Shen, Aiting [1 ]
机构：
[1] Anhui Univ, Sch Big Data & Stat, Hefei 230601, Peoples R China
[2] Chizhou Univ, Sch Big Data & Artificial Intelligence, Chizhou, Peoples R China
来源：
STOCHASTIC MODELS | 2024年
基金：
中国国家自然科学基金;
关键词：
Heteroscedastic; partially linear errors-in-variables model; rates of strong consistency; widely orthant dependent random variables; NEGATIVE ASSOCIATION; COMPLETE CONVERGENCE; RUIN PROBABILITIES; REGRESSION MODELS; WEIGHTED SUMS; LS ESTIMATOR; ASYMPTOTICS;
D O I：
10.1080/15326349.2024.2353062
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this article, some general results on the rates of strong consistency for the least squares estimators and weighted least squares estimators in heteroscedastic partially linear errors-in-variables model based on widely orthant dependent random errors are presented. The results can deduce strong consistency and convergence rates under some mild conditions. Some numerical simulations are also provided to support the theoretical results.
引用
收藏
页码：728 / 755
页数：28
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