The asymptotic properties of least square estimators in the linear errors-in-variables regression model with φ-mixing errors

doi:10.52396/JUST-2020-0019

Journal of University of Science and Technology of China ›› 2021, Vol. 51 ›› Issue (2): 164-172.DOI: 10.52396/JUST-2020-0019

• Research Articles: Mathematics • Previous Articles

The asymptotic properties of least square estimators in the linear errors-in-variables regression model with φ-mixing errors

Deng Xin¹, Tian Chunyu², Ge Meimei¹, Ye Jing¹, Ding Yang¹, Wu Yi^3*

1. School of Mathematical and Finance, Chuzhou University, Chuzhou 239000, China;
2. China Electronics Technology Group Corporation No.58 Research Institute, Nanjing 210000, China;
3. School of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, China

Received:2020-12-09 Revised:2021-01-30 Online:2021-02-28 Published:2021-11-16
Contact: * E-mail: wuyi8702@163.com

Abstract

Abstract: The simple linear errors-in-variables (EV) model with φ-mixing random errors was mainly studied. By using the central limit theorem and the Marcinkiewicz-type strong law of large numbers for the φ-mixing sequence, the asymptotic normality of the least square (LS) estimators for the unknown parameters were established under some mild conditions. In addition, based on the strong convergence for weighted sums of φ-mixing random variables, the strong consistency of the LS estimators were obtained. Finally, the simulation study was provided to verify the validity of the theoretical results.

Key words: EV model, asymptotic normality, strong consistency, LS estimator, φ-mixing sequence

CLC Number:

O211.4

DENG Xin, TIAN Chunyu, GE Meimei, YE Jing, DING Yang, WU Yi. The asymptotic properties of least square estimators in the linear errors-in-variables regression model with φ-mixing errors[J]. Journal of University of Science and Technology of China, 2021, 51(2): 164-172.

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The asymptotic properties of least square estimators in the linear errors-in-variables regression model with φ-mixing errors

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