Asymptotic normality and mean consistency of LS estimators in the errors-in-variables model with dependent errors

被引：4

作者：

Zhang, Yu ^{[1
]}

Liu, Xinsheng ^{[1
]}

Yu, Yuncai ^{[1
]}

Hu, Hongchang ^{[2
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Dept Math, State Key Lab Mech & Control Mech Struct, Nanjing, Peoples R China

[2] Hubei Normal Univ, Coll Math & Stat, Huangshi, Hubei, Peoples R China

来源：

OPEN MATHEMATICS | 2020年 / 18卷

基金：

中国国家自然科学基金;

关键词：

errors-in-variables model; negatively superadditive dependent; asymptotic normality; mean consistency; strong law of large numbers; EV REGRESSION-MODEL; NEGATIVE SUPERADDITIVE DEPENDENCE; WEAK CONSISTENCY; COMPLETE CONVERGENCE; ARRAYS;

D O I：

10.1515/math-2020-0052

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this article, an errors-in-variables regression model in which the errors are negatively superadditive dependent (NSD) random variables is studied. First, the Marcinkiewicz-type strong law of large numbers for NSD random variables is established. Then, we use the strong law of large numbers to investigate the asymptotic normality of least square (LS) estimators for the unknown parameters. In addition, the mean consistency of LS estimators for the unknown parameters is also obtained. Some results for independent random variables and negatively associated random variables are extended and improved to the case of NSD setting. At last, two simulations are presented to verify the asymptotic normality and mean consistency of LS estimators in the model.

引用

页码：930 / 947

页数：18

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