On consistency of the weighted estimator in nonparametric regression model with asymptotically almost negatively associated random variables

2021 ◽  
pp. 1-17
Author(s):  
Liwang Ding ◽  
Ping Chen
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Random Variables ◽  
Nonparametric Regression Model ◽  
Associated Random Variables ◽  
Weighted Estimator ◽  
Negatively Associated ◽  
Negatively Associated Random Variables ◽  
Asymptotically Almost Negatively Associated

scholarly journals Complete Convergence for Arrays of Rowwise Asymptotically Almost Negatively Associated Random Variables

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Xuejun Wang ◽  
Shuhe Hu ◽  
Wenzhi Yang
Keyword(s):  
Complete Convergence ◽  
Sufficient Conditions ◽  
Random Variables ◽  
Weighted Sums ◽  
Strong Law ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Negatively Associated Random Variables ◽  
Large Numbers ◽  
Asymptotically Almost Negatively Associated

Let{Xni,i≥1,n≥1}be an array of rowwise asymptotically almost negatively associated random variables. Some sufficient conditions for complete convergence for arrays of rowwise asymptotically almost negatively associated random variables are presented without assumptions of identical distribution. As an application, the Marcinkiewicz-Zygmund type strong law of large numbers for weighted sums of asymptotically almost negatively associated random variables is obtained.

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.

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