Limit theorems with rate of convergence under sublinear expectations

By Xiao Fang, Shige Peng, Qi-Man Shao, Yongsheng Song. Bernoulli, 2017

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Under the sublinear expectation $\mathbb{E}[\cdot]:=\sup_{\theta\in \Theta} E_\theta[\cdot]$ for a given set of linear expectations $\{E_\theta: \theta\in \Theta\}$, we establish a new law of large numbers and a new central limit theorem with rate of convergence. We present some interesting special cases and discuss a related statistical inference problem. We also give an approximation and a representation of the $G$-normal distribution, which was used as the limit in Peng (2007)'s central limit theorem, in a probability space.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ddbb1c59ebf7a986a7462c7f4f3299ff20b7d789