An invariance principle of strong law of large numbers under nonadditive probabilities

Xiaoyan Chen, Zengjing Chen and Liying Ren

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 10, 2398-2418

Abstract: In the framework of nonadditive probabilities or sublinear expectations, the Kolmogorov’s strong law of large numbers (SLLN) states that for a sequence of independent and identically distributed (IID) random variables, limit points of its sample mean quasi-surely fall inside an interval given by a pair of lower and upper means. In this article, we will investigate a cluster set of limit points of a sequence of stochastic processes, which are given by linear interpolating of the sample mean of IID random variables under sublinear expectations, and show an invariance principle. The invariance principle will strengthen the Kolmogorov’s SLLN under nonadditive probabilities in some extent.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2019.1669805 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:50:y:2021:i:10:p:2398-2418

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2019.1669805

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().