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 数学进展 - 2020, Vol. 49(4): 497-511
 研究论文
 次线性期望空间下END随机变量加权和的极限定理 Limiting Behavior of Weighted Sums of Extended Negatively Dependent Random Variables under Sublinear Expectations 马晓晨, 吴群英 MA Xiaochen*, WU Qunying** 桂林理工大学理学院, 桂林, 广西, 541004 College of Science, Guilin University of Technology, Guilin, Guangxi, 541004, P. R. China 收稿日期: 2019-06-21 出版日期: 2020-08-11 2020, Vol. 49(4): 497-511 DOI: 10.11845/sxjz.2019071b
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Abstract：In this article, our purpose is to establish the very extensive version of the strong law of large numbers (SLLN) of extended negatively dependent (END) random variables in the general sublinear expectation space. We obtain SLLN for END random variables under sublinear expectation with the upper integral condition of $C_{\mathbb{V}}(\varphi^{-}(|X|))<\infty$, where $\varphi(x)=x^{\frac{1}{\beta}}l(x)$. In addition, the results generalize corresponding results in [J. Math. Res. Exposition, 2011, 31(6): 1081-1091] to the sublinear expectations.
Key wordssublinear expectation    strong law of large numbers    END random variables
 PACS: O211.4

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