ON CONSISTENCY OF LS ESTIMATORS IN THE ERRORS-IN-VARIABLE REGRESSION MODEL

2016 ◽  
Vol 32 (1) ◽  
pp. 144-162 ◽  
Author(s):  
Xuejun Wang ◽  
Mengmei Xi ◽  
Hongxia Wang ◽  
Shuhe Hu
Keyword(s):  
Regression Model ◽  
Simulation Study ◽  
Strong Consistency ◽  
Random Variables ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Complete Consistency ◽  
Mild Conditions ◽  
Negatively Associated Random Variables

Under some mild conditions, the strong consistency and complete consistency of the LS estimators in the errors-in-variable regression model with weakly negative dependent errors are obtained, which generalize the corresponding ones for negatively associated random variables. In addition, the simulation study shows that the biases of our method are small, and our method performs well.

COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF ROWWISE NEGATIVELY ASSOCIATED RANDOM VARIABLES AND ITS APPLICATION IN NON-PARAMETRIC REGRESSION MODEL

2017 ◽  
Vol 32 (1) ◽  
pp. 37-57 ◽  
Author(s):  
Yi Wu ◽  
Xuejun Wang ◽  
Soo Hak Sung
Keyword(s):  
Numerical Simulation ◽  
Regression Model ◽  
Random Variables ◽  
Complete Moment Convergence ◽  
Parametric Regression ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Complete Consistency ◽  
Moment Convergence ◽  
Non Parametric

In this paper, some results on the complete moment convergence for arrays of rowwise negatively associated (NA, for short) random variables are established. The results obtained in this paper correct the corresponding one obtained in Ko [13] and also improve and generalize the corresponding ones of Kuczmaszewska [14] and Ko [13]. As an application of the main results, we present a result on complete consistency for the estimator in a non-parametric regression model based on NA errors. Finally, we provide a numerical simulation to verify the validity of our result.

Summability methods and negatively associated random variables

2004 ◽  
Vol 41 (A) ◽  
pp. 231-238
Author(s):  
N. H. Bingham ◽  
H. R. Nili Sani
Keyword(s):  
Random Variables ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Summability Methods ◽  
Negatively Associated Random Variables ◽  
Convergence Of Sequences

The paper studies convergence of sequences of negatively associated random variables under various summability methods. The results extend previously known results for independence and complement known results forϕ-mixing.

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