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arxiv: 0803.2656 · v1 · submitted 2008-03-18 · 🧮 math.PR · math.ST· stat.TH

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A New Central Limit Theorem under Sublinear Expectations

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classification 🧮 math.PR math.STstat.TH
keywords sublineartheoremcentralexpectationlimitmean-uncertaintyundercase
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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.

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  1. Distributional Statistical Models: Weak Moments, Cumulants, and a Central Limit Theorem

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    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.