Cramér Moderate Deviation Expansion for Martingales with One-Sided Sakhanenko’s Condition and Its Applications
Xiequan Fan (),
Ion Grama () and
Quansheng Liu ()
Additional contact information
Xiequan Fan: Tianjin University
Ion Grama: Univ. Bretagne-Sud
Quansheng Liu: Univ. Bretagne-Sud
Journal of Theoretical Probability, 2020, vol. 33, issue 2, 749-787
Abstract:
Abstract We give a Cramér moderate deviation expansion for martingales with differences having finite conditional moments of order $$2+\rho , \rho \in (0,1]$$2+ρ,ρ∈(0,1], and finite one-sided conditional exponential moments. The upper bound of the range of validity and the remainder of our expansion are both optimal. Consequently, our result leads to a one-sided moderate deviation principle for martingales. Moreover, applications to quantile coupling inequality, $$\beta $$β-mixing sequences and $$\psi $$ψ-mixing sequences are discussed.
Keywords: Martingales; Cramér moderate deviations; Quantile coupling inequality; $$\beta $$ β -Mixing sequences; $$\psi $$ ψ -Mixing sequences; 60G42; 60F10; 60E15; 60F05 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10959-019-00949-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:jotpro:v:33:y:2020:i:2:d:10.1007_s10959-019-00949-2
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10959
DOI: 10.1007/s10959-019-00949-2
Access Statistics for this article
Journal of Theoretical Probability is currently edited by Andrea Monica
More articles in Journal of Theoretical Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().