标题：Limit Theorems for m-Dependent Random Variables under Subliner Expectations

摘要:

This thesis develops nonlinear probability theory by systematically establishing limit theorems for m-dependent random variables under sublinear expectations. Its main contributions include a generalized central limit theorem under weakened moment assumptions, a new strong law of large numbers with a necessary and sufficient condition, and complete limit theorems for the law of the iterated logarithm and Strassen’s law of the iterated logarithm under the finite second-order Choquet integral condition. These results provide a robust mathematical foundation for modeling distributional uncertainty in applications such as financial risk management.