Asymptotic properties for LS estimators in EV regression model with dependent errors

被引：42

作者：

Fan, Guo-Liang ^{[1
]}

Liang, Han-Ying ^{[2
]}

Wang, Jiang-Feng ^{[3
]}

Xu, Hong-Xia ^{[1
]}

机构：

[1] Anhui Univ Technol & Sci, Dept Appl Math & Phys, Wuhu 241000, Peoples R China

[2] Tongji Univ, Dept Math, Shanghai 200092, Peoples R China

[3] Hangzhou Normal Univ, Sch Sci, Hangzhou 310036, Zhejiang, Peoples R China

来源：

ASTA-ADVANCES IN STATISTICAL ANALYSIS | 2010年 / 94卷 / 01期

基金：

中国国家自然科学基金;

关键词：

EV regression models; LS estimator; alpha-mixing sequence; Strong consistency; Asymptotic normality; IN-VARIABLES MODELS; MULTIVARIATE ERRORS; CONSISTENCY; CONVERGENCE; SEQUENCES;

D O I：

10.1007/s10182-010-0124-3

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 establish the strong consistency and asymptotic normality for the least square (LS) estimators in simple linear errors-in-variables (EV) regression models when the errors form a stationary alpha-mixing sequence of random variables. The quadratic-mean consistency is also considered.

引用

页码：89 / 103

页数：15

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