Several Different Types of Convergence for ND Random Variables under Sublinear Expectations | Zendy

Ziwei Liang | Zendy; Qunying Wu | Zendy

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Several Different Types of Convergence for ND Random Variables under Sublinear Expectations

Author(s) -

Ziwei Liang,

Qunying Wu

Publication year - 2021

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2021/6653435

Subject(s) - sublinear function , mathematics , convergence (economics) , random variable , convergence of random variables , markov's inequality , space (punctuation) , markov chain , proofs of convergence of random variables , discrete mathematics , inequality , computer science , statistics , sum of normally distributed random variables , rearrangement inequality , log sum inequality , mathematical analysis , economics , economic growth , operating system

The goal of this paper is to build average convergence and almost sure convergence for ND (negatively dependent) sequences of random variables under sublinear expectation space. By using the basic definition of sublinear expectation space, Markov inequality, and C r inequality, we extend average convergence and almost sure convergence theorems for ND sequences of random variables under sublinear expectation space, and we provide a way to learn this subject.

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