Central limit theorems for LS estimators in the EV regression model with dependent measurements

被引：0
|
作者：
Yu Miao
Fangfang Zhao
Ke Wang
机构：
[1] Henan Normal University,College of Mathematics and Information Science
来源：
Journal of the Korean Statistical Society | 2011年 / 40卷
关键词：
62F12; 60F05; EV model; Asymptotic normality; LS estimator; -dependent; Martingale differences; -mixing; -mixing; -mixing;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we consider the simple linear errors-in-variables (EV) regression models: ηi = θ + βχi + εi, ξi = χi + δi, 1 ≤ i ≤ n, where θ, β, χ1, χ2, … are unknown constants (parameters), (ε1, δ1), (ε2, δ2), … are errors and ξi, ηi, i = 1, 2, … are observable. The asymptotic normality for the least square (LS) estimators of the unknown parameters β and θ in the model are established under the assumptions that the errors are m-dependent, martingale differences, ϕ-mixing, ρ-mixing and α-mixing.
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页码：303 / 312
页数：9
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