Document Detail

Bayesian inferences on umbrella orderings.
MedLine Citation:
PMID:  16401275     Owner:  NLM     Status:  MEDLINE    
In regression applications with categorical predictors, interest often focuses on comparing the null hypothesis of homogeneity to an ordered alternative. This article proposes a Bayesian approach for addressing this problem in the setting of normal linear and probit regression models. The regression coefficients are assigned a conditionally conjugate prior density consisting of mixtures of point masses at 0 and truncated normal densities, with a (possibly unknown) changepoint parameter included to accommodate umbrella ordering. Two strategies of prior elicitation are considered: (1) a Bayesian Bonferroni approach in which the probability of the global null hypothesis is specified and local hypotheses are considered independent; and (2) an approach which treats these probabilities as random. A single Gibbs sampling chain can be used to obtain posterior probabilities for the different hypotheses and to estimate regression coefficients and predictive quantities either by model averaging or under the preferred hypothesis. The methods are applied to data from a carcinogenesis study.
Chris Hans; David B Dunson
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  61     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2005 Dec 
Date Detail:
Created Date:  2006-01-10     Completed Date:  2006-03-07     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1018-26     Citation Subset:  IM    
Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27708, USA.
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MeSH Terms
Adenoma, Liver Cell / chemically induced
Bayes Theorem*
Biological Assay / methods
Curcuma / metabolism,  toxicity
Data Interpretation, Statistical*
Models, Statistical*
Plant Extracts / metabolism,  toxicity
Reg. No./Substance:
0/Plant Extracts; 8024-37-1/turmeric extract

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