Asymptotic normality and mean consistency of LS estimators in the errors-in-variables model with dependent errors-Reference-Cited by-同舟云学术

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

Published:2020-01-01 Issue:1 Volume:18 Page:930-947
ISSN:2391-5455
Container-title:Open Mathematics
language:en
Short-container-title:

Author:

Zhang Yu¹,Liu Xinsheng¹,Yu Yuncai¹,Hu Hongchang²

Affiliation:

1. State Key Laboratory of Mechanics and Control of Mechanical Structures, Department of Mathematics, Nanjing University of Aeronautics and Astronautics , Nanjing , China

2. College of Mathematics and Statistics, Hubei Normal University, Huangshi , China

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Subject

General Mathematics

Link

https://www.degruyter.com/document/doi/10.1515/math-2020-0052/pdf

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