The parameter estimations for uncertain regression model with autoregressive time series errors

Uncertain regression model with autoregressive time series errors studies the relations between response variables and explanatory variables when the errors are dependence. The estimations of the unknown parameters are worth exploring in further research. The least square (LS) estimations were propo...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2024-07, Vol.53 (13), p.4841-4856
Main Authors: Shi, Yuxin, Sheng, Yuhong
Format: Article
Language:English
Subjects:
Errors
GDP
Least squares
Maximum likelihood estimation
Outliers (statistics)
Parameter estimation
Parameter uncertainty
prediction
Regression models
the least deviation estimation
the maximum likelihood estimation
Time series
Uncertainty theory
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Summary:Uncertain regression model with autoregressive time series errors studies the relations between response variables and explanatory variables when the errors are dependence. The estimations of the unknown parameters are worth exploring in further research. The least square (LS) estimations were proposed to estimate the unknown parameters. However, when the outliers appear, we find that the LS is not effective. So, in this article, the least absolute deviations (LAD) and the maximum likelihood estimations (MLE) are proposed to estimate the unknown parameters. Moreover, some examples are given to verify the applicability and practicability of these two methods. The comparative analysis is given to verify that the proposed methods are more advantageous and effective than the least square method when the observed data are affected by outliers. Finally, the example of exploring the relationships between GDP and related influencing factors for China, from 2004 to 2020, is given to testify the practicability of the methods.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2023.2195034