Conditional large deviations for density case

Gie-Whan Kim and Donald R. Truax

Statistics & Probability Letters, 1998, vol. 38, issue 2, 137-144

Abstract: The conditional large deviations theorem of Jing and Robinson (1994) is extended in the following sense. Consider a random sample of pairs of random vectors and the sample means of each of the pairs. For p [greater-or-equal, slanted] 1, the probability that first falls outside a certain p-dimensional convex set given that the second is fixed is shown to decrease with the sample size at an exponential rate which depends on the Kullback-Leibler distance between two distributions in an associated exponential familiy of distributions. Examples are given which include a method of computing the Bahadur exact slope for tests of certain composite hypotheses in exponential families.

Keywords: Large; deviations; Exponential; families; Testing; hypotheses; Kullback-Leibler; information (search for similar items in EconPapers)
Date: 1998
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(97)00164-8
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:stapro:v:38:y:1998:i:2:p:137-144

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

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().