A self-normalized central limit theorem for a ρ-mixing stationary sequence

Guang-hui Cai, Lin Xiang, Xin-xing Su and Xue-hai Ying

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 8, 1785-1791

Abstract: We investigate a self-normalized central limit theorem for a ρ-mixing stationary sequence {Xi, i ⩾ 1} of random variables such that L(x) ≔ E(X21I{|X1| ⩽ x}) is a slowly varying function as x → ∞. The results obtained generalize the results of Gine, Gotze, and Mason (1997) and Mason (2005) to ρ-mixing sequences.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2016.1277754 (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:47:y:2018:i:8:p:1785-1791

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

DOI: 10.1080/03610926.2016.1277754

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 ().