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Local robustness of Bayesian parametric inference and observed likelihoods
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Smith, J. Q. (2007) Local robustness of Bayesian parametric inference and observed likelihoods. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2007 (No.9).

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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
Abstract
Here a new class of local separation measures over prior densities is
studied and their usefulness for examining prior to posterior robustness
under a sequence of observed likelihoods, possibly erroneous, illustrated.
It is shown that provided an approximation to a prior distribution satisfies certain mild smoothness and tail conditions then prior to posterior
inference for large samples is robust, irrespective of whether the priors
are grossly misspecified with respect to variation distance and irrespective of the form or the validity of the observed likelihood. Furthermore
it is usually possible to specify error bounds explicitly in terms of statistics associated with the posterior associated with the approximating prior
and asumed prior error bounds. These results apply in a general multivariate setting and are especially easy to interpret when prior densities
are approximated using standard families or multivariate prior densities
factorise.
Item Type:  Working or Discussion Paper (Working Paper) 

Subjects:  Q Science > QA Mathematics 
Divisions:  Faculty of Science > Statistics 
Library of Congress Subject Headings (LCSH):  Distribution (Probability theory), Bayesian statistical decision theory 
Series Name:  Working papers 
Publisher:  University of Warwick. Centre for Research in Statistical Methodology 
Place of Publication:  Coventry 
Official Date:  2007 
Volume:  Vol.2007 
Number:  No.9 
Number of Pages:  39 
Status:  Not Peer Reviewed 
Access rights to Published version:  Open Access 
References:  [1] Andrade, J. A. A. and O'Hagan, A. (2006). Bayesian robustness modelling 
URI:  http://wrap.warwick.ac.uk/id/eprint/35541 
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