The Consistency of LSE Estimators in Partial Linear Regression Models under Mixing Random Errors

被引：0
作者：
Yun Bao Yao
Yu Tan Lü
Chao Lu
Wei Wang
Xue Jun Wang
机构：
[1] Anhui University,School of Mathematical Sciences
[2] Chizhou University,School of Big Data and Artificial Intelligence
[3] Anhui University,School of Big Data and Statistics
来源：
Acta Mathematica Sinica, English Series | 2024年 / 40卷
关键词：
(; , ; )-mixing random variables; partial linear regression model; least squares estimator; consistency; 62G05;
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摘要：
In this paper, we consider the partial linear regression model yi = xiβ* + g(ti) + εi, i = 1, 2, …, n, where (xi, ti) are known fixed design points, g(·) is an unknown function, and β* is an unknown parameter to be estimated, random errors εi are (α, β)-mixing random variables. The p-th (p > 1) mean consistency, strong consistency and complete consistency for least squares estimators of β* and g(·) are investigated under some mild conditions. In addition, a numerical simulation is carried out to study the finite sample performance of the theoretical results. Finally, a real data analysis is provided to further verify the effect of the model.
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页码：1244 / 1272
页数：28
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