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

被引：9
|
作者：
Miao, Yu [1 ]
Zhao, Fangfang [1 ]
Wang, Ke [1 ]
机构：
[1] Henan Normal Univ, Coll Math & Informat Sci, Xinxiang 453007, Henan Province, Peoples R China
来源：
JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2011年 / 40卷 / 03期
基金：
中国国家自然科学基金;
关键词：
EV model; Asymptotic normality; LS estimator; m-dependent; Martingale differences; phi-mixing; rho-mixing; alpha-mixing; ASYMPTOTIC PROPERTIES; SUMS;
D O I：
10.1016/j.jkss.2010.12.002
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, we consider the simple linear errors-in-variables (EV) regression models: eta(i) = theta + beta x(i) + epsilon(i), xi(i) = x(i) + delta(i), 1 <= i <= n, where theta, beta, x(1), x(2), ... are unknown constants (Parameters), (epsilon(1), delta(1)), (epsilon(2), delta(2)), ... are errors and xi(i), eta(i), i = 1, 2, ... are observable. The asymptotic normality for the least square (LS) estimators of the unknown parameters beta and theta in the model are established under the assumptions that the errors are m-dependent, martingale differences, phi-mixing, rho-mixing and alpha-mixing. (C) 2010 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：303 / 312
页数：10
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