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 Cv(φ-(|X|)) ＜ ∞,where φ(x) =x1/-βl(x).In addition,the results generalize corresponding results in[J.Math.Res.Exposition,2011,31(6):1081-1091]to the sublinear expectations.

研究次线性期望空间下END列加权和的完全收敛性,在随机变量的2+r/α阶上积分存在条件下,将概率空间中END列加权和的完全收敛性推广到了次线性期望空间.

In this article, we research some conditions for strong law of large numbers (SLLNs) for weighted sums of extended negatively dependent (END) random variables under sublinear expectation space. Our consequences contain the Kolmogorov strong law of large numbers and the Marcinkiewicz strong law of large numbers for weighted sums of extended negatively dependent random variables. Furthermore, our results extend strong law of large numbers for some sequences of random variables from the traditional probability space to the sublinear expectation space context.