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.

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.

scholarly journals Complete convergence and complete moment convergence for arrays of Rowwise asymptotically almost negatively associated random variables under the sub-linear expectations

Filomat ◽  
2021 ◽  
Vol 35 (2) ◽  
pp. 633-644
Author(s):  
Dawei Lu ◽  
Jingyao Cong ◽  
Yanchun Yang
Keyword(s):  
Complete Convergence ◽  
Random Variables ◽  
Partial Sums ◽  
Complete Moment Convergence ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Moment Convergence ◽  
Negatively Associated Random Variables ◽  
Asymptotically Almost Negatively Associated

In this article, we investigate the complete convergence and complete moment convergence for maximal partial sums of asymptotically almost negatively associated random variables under the sublinear expectations. The results obtained in the article are the extensions of the complete convergence and complete moment convergence under classical linear expectation space.

scholarly journals On Complete Moment Convergence of Weighted Sums for Arrays of Rowwise Negatively Associated Random Variables

2012 ◽  
Vol 2012 ◽  
pp. 1-12
Author(s):  
Mingle Guo ◽  
Dongjin Zhu
Keyword(s):  
Moving Average ◽  
Sufficient Conditions ◽  
Random Variables ◽  
Weighted Sums ◽  
Complete Moment Convergence ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Moment Convergence ◽  
Negatively Associated Random Variables ◽  
Moving Average Processes

The complete moment convergence of weighted sums for arrays of rowwise negatively associated random variables is investigated. Some sufficient conditions for complete moment convergence of weighted sums for arrays of rowwise negatively associated random variables are established. Moreover, the results of Baek et al. (2008), are complemented. As an application, the complete moment convergence of moving average processes based on a negatively associated random sequences is obtained, which improves the result of Li et al. (2004).

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