Multi-dimensional central limit theorems and laws of large numbers under sublinear expectations
Ze-Chun Hu, Ling Zhou
TL;DR
This paper extends classical probability theorems to multi-dimensional settings under sublinear expectations, broadening their applicability in uncertain environments.
Contribution
It introduces new multi-dimensional central limit theorems and laws of large numbers under sublinear expectations, expanding previous results.
Findings
Extended CLT to multi-dimensional sublinear expectations
Established multi-dimensional laws of large numbers under sublinear expectations
Generalized classical probabilistic results to uncertain environments
Abstract
In this paper, we present some multi-dimensional central limit theorems and laws of large numbers under sublinear expectations, which extend some previous results.
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Taxonomy
TopicsStochastic processes and statistical mechanics · Stochastic processes and financial applications · Probability and Risk Models