Document Detail


Some comments on misspecification of priors in Bayesian modelling of measurement error problems.
MedLine Citation:
PMID:  9004392     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this paper we discuss some aspects of misspecification of prior distributions in the context of Bayesian modelling of measurement error problems. A Bayesian approach to the treatment of common measurement error situations encountered in epidemiology has been recently proposed. Its implementation involves, first, the structural specification, through conditional independence relationships, of three submodels-a measurement model, an exposure model and a disease model- and secondly, the choice of functional forms for the distributions involved in the submodels. We present some results indicating how the estimation of the regression parameters of interest, which is carried out using Gibbs sampling, can be influenced by a misspecification of the parametric shape of the prior distribution of exposure.
Authors:
S Richardson; L Leblond
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  16     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:    1997 Jan 15-Feb 15
Date Detail:
Created Date:  1997-03-26     Completed Date:  1997-03-26     Revised Date:  2000-12-18    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  203-13     Citation Subset:  IM    
Affiliation:
Institut National de la Santé et de la Recherche Medicale-U.170, Villejuif, France.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Bayes Theorem*
Chi-Square Distribution
Data Interpretation, Statistical
Markov Chains
Models, Statistical*
Normal Distribution
Research Design
Risk Assessment
Stochastic Processes

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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