Abstract
ABSTRACTRecently, more and more researchers are interested in the investigation of strong laws of large numbers (SLLNs) under non additive probability. This article introduces a concept of negative dependence under sublinear expectations to investigate the SLLNs when the smallest subscript of random variables in the sample mean can change. It proves that all the cluster points of that kind of sample mean lie between an interval related to lower and upper means (or limits of sums of lower and upper means) of random variables with probability one under a lower probability.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
