Weak convergence for weighted sums of negatively associated random variables and its application in nonparametric regression models

2020 ◽  
pp. 1-21
Author(s):  
Lu Zhang ◽  
Jibing Qi ◽  
Hairong Yang ◽  
Xuejun Wang
Keyword(s):  
Weak Convergence ◽  
Nonparametric Regression ◽  
Regression Models ◽  
Random Variables ◽  
Weighted Sums ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Negatively Associated Random Variables

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 the Almost sure Convergence Rate for Weighted Sums of Negatively Associated Random Variables with Application to Priestley-Chao Estimate

2015 ◽  
Vol 742 ◽  
pp. 449-452
Author(s):  
Gan Ji Huang ◽  
Guo Dong Xing
Keyword(s):  
Convergence Rate ◽  
Random Variables ◽  
Almost Sure Convergence ◽  
Weighted Sums ◽  
Exponential Inequality ◽  
Regression Estimate ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Negatively Associated Random Variables ◽  
Existing Result

This paper deals with the problem of almost sure convergence rate for weighted sums of negatively associated random variables. A new convergence rate is obtained base on an exponential inequality, the result obtained extends and has a fast convergence rate compare with the existing result. As an application, we study the Priestley-Chao estimate of nonparametric regression estimate and the convergence rate is derived.

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