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Approximate bayesian inference in semiparametric copula models

Abstract:
We describe a simple method for making inference on a functional of a multivariate distribution, based on its copula representation. We make use of an approximate Bayesian Monte Carlo algorithm, where the proposed values of the functional of interest are weighted in terms of their Bayesian exponentially tilted empirical likelihood. This method is particularly useful when the "true" likelihood function associated with the working model is too costly to evaluate or when the working model is only partially specified.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1214/17-BA1080

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
NDM Experimental Medicine
Role:
Author
More from this funder
Name:
Ministero dell’Istruzione dell’Universit`a e della Ricerca, Italia
Grant:
PRIN 2015
More from this funder
Name:
Royal Society
Grant:
“Empirical
BootstrapLikelihoodproceduresforApproximateBayesianInference” (2015
Publisher:
International Society for Bayesian Analysis
Journal:
Bayesian Analysis More from this journal
Volume:
12
Issue:
4
Pages:
991-1016
Publication date:
2017-12-01
Acceptance date:
2017-01-01
DOI:
EISSN:
1931-6690
ISSN:
1936-0975
Keywords:
Pubs id:
pubs:808869
UUID:
uuid:f51c7e4e-4a3b-40a9-94cb-bf659cb801c1
Local pid:
pubs:808869
Source identifiers:
808869
Deposit date:
2018-01-31

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