Abstract
In this paper, we study weak laws of large numbers for random variables under model uncertainty. We introduce the concepts of conditional upper and lower expectations induced by a stable set of probability measures, and obtain weak laws of large numbers for arrays of random variables without the assumption of identical distribution. It shows that, under model uncertainty, the sample average of the observed random variables has its limit points within an identified set determined by the limits of upper and lower expectations. Further, we illustrate an application of our result to m-dependent random variables and obtain a general weak law of large numbers for sequences of m-dependent random variables.
Sign-in/Register to access full text options 
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
