Strong Convergence Properties for Weighted Sums of Extended Negatively Dependent Random Variables Under Sub-linear Expectations with Statistical Applications

被引：0

作者：

Li, Liangxue ^{[1
]}

Zheng, Xiaoqian ^{[1
]}

Huang, Haiwu ^{[2
]}

Wang, Xuejun ^{[1
]}

机构：

[1] Anhui Univ, Sch Big Data & Stat, Hefei 230601, Peoples R China

[2] Guilin Univ Aerosp Technol, Sch Sci, Guilin 541004, Peoples R China

来源：

METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY | 2024年 / 26卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Complete f-moment convergence; Sub-linear expectations; END random variables; von Bahr-Esseen type inequality; Strong law of large numbers; Consistency; Nonparametric regression model; COMPLETE MOMENT CONVERGENCE; G-BROWNIAN MOTION; STOCHASTIC CALCULUS;

D O I：

10.1007/s11009-024-10092-z

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 paper, we establish the complete f-moment convergence and the Marcinkiewicz-Zygmund type strong law of large numbers for weighted sums of extended negatively dependent (END, for short) random variables under sub-linear expectations, which extend and improve corresponding ones in sub-linear expectation space. As applications of the main results, the complete consistency and strong consistency of weighted estimators in nonparametric regression models under sub-linear expectations are also obtained.

引用

页数：36

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