Theorems of complete convergence and complete integral convergence for END random variables under sub-linear expectations

Abstract

The goal of this paper is to build complete convergence and complete integral convergence for END sequences of random variables under sub-linear expectation space. By using the Markov inequality, we extend some complete convergence and complete integral convergence theorems for END sequences of random variables when we have a sub-linear expectation space, and we provide a way to learn this subject.

Complete Convergence for Weighted Sums of Widely Acceptable Random Variables under Sublinear Expectations

“…Chen [10] obtains a SLLN for an independent identically distributed sequence in the sublinear expectations space. Liang and Wu [11] research on complete convergence and complete integral convergence for extended negatively dependent (END) random variables under sublinear expectations.…”