Open AccessComplete convergence and complete moment convergence for negatively dependent random variables under sub-linear expectations
Author(s) -
Qunying Wu,
Yuanying Jiang
Publication year - 2020
Publication title -
filomat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 34
eISSN - 2406-0933
pISSN - 0354-5180
DOI - 10.2298/fil2004093w
Subject(s) - mathematics , convergence (economics) , moment (physics) , proofs of convergence of random variables , compact convergence , convergence tests , modes of convergence (annotated index) , convergence of random variables , normal convergence , context (archaeology) , space (punctuation) , weak convergence , random variable , discrete mathematics , rate of convergence , statistics , sum of normally distributed random variables , topological space , computer science , channel (broadcasting) , computer security , isolated point , asset (computer security) , economic growth , computer network , topological vector space , biology , operating system , paleontology , classical mechanics , physics , economics
This paper we study and establish the complete convergence and complete moment convergence theorems under a sub-linear expectation space. As applications, the complete convergence and complete moment convergence for negatively dependent random variables with CV (exp (ln? |X|)) 1 have been generalized to the sub-linear expectation space context. We extend some complete convergence and complete moment convergence theorems for the traditional probability space to the sub-linear expectation space. Our results generalize corresponding results obtained by Gut and Stadtm?ller (2011), Qiu and Chen (2014) and Wu and Jiang (2016). There is no report on the complete moment convergence under sub-linear expectation, and we provide the method to study this subject.