Objective Bayesian testing for the linear combinations of normal means
Woo Dong Lee,
Sang Gil Kang and
Yongku Kim ()
Additional contact information
Woo Dong Lee: Daegu Haany University
Sang Gil Kang: Sangji University
Yongku Kim: Kyungpook National University
Statistical Papers, 2019, vol. 60, issue 1, No 9, 147-172
Abstract:
Abstract This study considers objective Bayesian testing for the linear combinations of the means of several normal populations. We propose solutions based on a Bayesian model selection procedure to this problem in which no subjective input is considered. We first construct suitable priors to test the linear combinations of means based on measuring the divergence between competing models (so-called divergence-based priors). Next, we derive the intrinsic priors for which the Bayes factors and model selection probabilities are well defined. Finally, the behavior of the Bayes factors based on the DB priors, intrinsic priors, and classical test are compared in a simulation study and an example.
Keywords: Bayes factor; Divergence-based prior; Intrinsic prior; Linear combinations of normal means; Reference prior (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s00362-016-0831-2 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:stpapr:v:60:y:2019:i:1:d:10.1007_s00362-016-0831-2
Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362
DOI: 10.1007/s00362-016-0831-2
Access Statistics for this article
Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller
More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().