Intrinsic priors for testing exponential means

Seong W. Kim

Statistics & Probability Letters, 2000, vol. 46, issue 2, 195-201

Abstract: In Bayesian model selection or testing problems of different dimensions, one cannot utilize standard or default noninformative priors, since these priors are typically improper and are defined only up to arbitrary constants. The resulting Bayes factors are not well defined. A recently proposed model selection criterion called the intrinsic Bayes factor, overcomes such problems by using a data-splitting idea. Furthermore, the intrinsic Bayes factors are asymptotically equal to the ordinary Bayes factors computed by some reasonable proper priors called intrinsic priors. Several intrinsic priors are derived for testing the equality of two independent exponential means. We demonstrate our results with a simulated dataset.

Keywords: Intrinsic; Bayes; factor; Intrinsic; priors; Jeffreys's; priors; Noninformative; priors (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(99)00106-6
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:46:y:2000:i:2:p:195-201

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 ().