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Complete convergence for weighted sums of END random variables and its application to nonparametric regression models

机译:END随机变量加权和的完全收敛及其在非参数回归模型中的应用

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摘要

In this article, the complete convergence for weighted sums of extended negatively dependent (END, for short) random variables is investigated. Some sufficient conditions for the complete convergence are provided. In addition, the Marcinkiewicz-Zygmund type strong law of large numbers for weighted sums of END random variables is obtained. The results obtained in the article generalise and improve the corresponding one of Wang et al. [(2014b), On Complete Convergence for an Extended Negatively Dependent Sequence', Communications in Statistics-Theory and Methods, 43, 2923-2937]. As an application, the complete consistency for the estimator of nonparametric regression model is established.
机译:在本文中,研究了扩展的负相关变量(简称END)随机变量的加权和的完全收敛性。为完全收敛提供了一些充分的条件。另外,获得了END随机变量加权和的Marcinkiewicz-Zygmund型强数定律。文章中获得的结果推广并改进了Wang等人的相应文章。 [(2014b),关于扩展的负相关序列的完全收敛性,《统计理论与方法》,第43卷,2923-2937年]。作为一个应用,建立了非参数回归模型估计量的完全一致性。

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