scholarly journals A Note on LS Berry-Esseen Estimator in Simple Linear EV Regression Model

2015 ◽  
Vol 04 (01) ◽  
pp. 29-36
Author(s):  
娇 孟
Keyword(s):  
Regression Model ◽  
Ev Regression Model

Asymptotic for LS estimators in the EV regression model for dependent errors

Filomat ◽  
2017 ◽  
Vol 31 (15) ◽  
pp. 4845-4856
Author(s):  
Konrad Furmańczyk
Keyword(s):  
Regression Model ◽  
Functional Dependence ◽  
Dependent Data ◽  
Errors In Variables ◽  
Mixing Conditions ◽  
Asymptotic Results ◽  
Dependence Measure ◽  
Wide Range ◽  
Ev Regression Model ◽  
Linear And Nonlinear

We study consistency and asymptotic normality of LS estimators in the EV (errors in variables) regression model under weak dependent errors that involve a wide range of linear and nonlinear time series. In our investigations we use a functional dependence measure of Wu [16]. Our results without mixing conditions complete the known asymptotic results for independent and dependent data obtained by Miao et al. [7]-[10].

Consistency of LS estimators in the EV regression model with martingale difference errors

Statistics ◽  
2014 ◽  
Vol 49 (1) ◽  
pp. 104-118 ◽  
Author(s):  
Yu Miao ◽  
Yanling Wang ◽  
Haojiang Zheng
Keyword(s):  
Regression Model ◽  
Martingale Difference ◽  
Ev Regression Model

scholarly journals Asymptotic normality of a simple linear EV regression model with martingale difference errors

Filomat ◽  
2014 ◽  
Vol 28 (9) ◽  
pp. 1817-1825
Author(s):  
Guo-Liang Fan ◽  
Tian-Heng Chen
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Measurement Errors ◽  
Least Squares Method ◽  
Error Variance ◽  
Martingale Difference ◽  
Errors In Variables ◽  
Ev Regression Model ◽  
Biased Estimators ◽  
Better Than

This paper considers the estimation of a linear EV (errors-in-variables) regression model under martingale difference errors. The usual least squares estimations lead to biased estimators of the unknown parametric when measurement errors are ignored. By correcting the attenuation we propose a modified least squares estimator for a parametric component and construct the estimators of another parameter component and error variance. The asymptotic normalities are also obtained for these estimators. The simulation study indicates that the modified least squares method performs better than the usual least squares method.

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

2010 ◽  
Vol 94 (1) ◽  
pp. 89-103 ◽  
Author(s):  
Guo-Liang Fan ◽  
Han-Ying Liang ◽  
Jiang-Feng Wang ◽  
Hong-Xia Xu
Keyword(s):  
Regression Model ◽  
Asymptotic Properties ◽  
Ev Regression Model
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