Recognition: unknown
A New Central Limit Theorem under Sublinear Expectations
read the original abstract
We describe a new framework of a sublinear expectation space and the related notions and results of distributions, independence. A new notion of G-distributions is introduced which generalizes our G-normal-distribution in the sense that mean-uncertainty can be also described. W present our new result of central limit theorem under sublinear expectation. This theorem can be also regarded as a generalization of the law of large number in the case of mean-uncertainty.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Distributional Statistical Models: Weak Moments, Cumulants, and a Central Limit Theorem
A distributional framework using tempered distributions and Schwartz kernels defines weak moments and cumulants, supports a weak central limit theorem, and gives consistent location estimation for the Cauchy distribution.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.