Exponential approximations in completely regular topological spaces and extensions of Sanov's theorem

Peter Eichelsbacher and Uwe Schmock

Stochastic Processes and their Applications, 1998, vol. 77, issue 2, 233-251

Abstract: This paper is devoted to the well known transformations that preserve a large deviation principle (LDP), namely, the contraction principle with approximately continuous maps and the concepts of exponential equivalence and exponential approximations. We generalize these transformations to completely regular topological state spaces, give some examples and, as an illustration, reprove a generalization of Sanov's theorem, due to de Acosta (J. Appl. Probab. 31 A (1994) 41-47). Using partition-dependent couplings, we then extend this version of Sanov's theorem to triangular arrays and prove a full LDP for the empirical measures of exchangeable sequences with a general measurable state space.

Keywords: Large; deviations; Exponential; equivalence; Contraction; principle; Gauge; space; Uniform; space; Approximately; continuous; map; Triangular; array; Exchangeable; sequence (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304-4149(98)00047-7
Full text for ScienceDirect subscribers only

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:eee:spapps:v:77:y:1998:i:2:p:233-251

Ordering information: This journal article can be ordered from
http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().