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

Published online by Cambridge University Press:  16 January 2017

Yi Wu ,
Xuejun Wang  and
Soo Hak Sung
Yi Wu
Affiliation:
School of Mathematical Sciences, Anhui University, Hefei 230601, People's Republic of China E-mail: [email protected]
Xuejun Wang
Affiliation:
School of Mathematical Sciences, Anhui University, Hefei 230601, People's Republic of China E-mail: [email protected]
Soo Hak Sung
Affiliation:
Department of Applied Mathematics, Pai Chai University, Taejon 302-735, South Korea
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Abstract

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.

Keywords

complete consistencycomplete convergencecomplete moment convergencenegatively associated random variablesnon-parametric regression model
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 32 , Issue 1 , January 2018 , pp. 37 - 57
Copyright
Copyright © Cambridge University Press 2017 

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