Document Detail

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets.
MedLine Citation:
PMID:  25303085     Owner:  NLM     Status:  Publisher    
Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1-28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.
Xing Ju Lee; Christopher C Drovandi; Anthony N Pettitt
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-10-9
Journal Detail:
Title:  Biometrics     Volume:  -     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2014 Oct 
Date Detail:
Created Date:  2014-10-10     Completed Date:  -     Revised Date:  2014-10-11    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© 2014, The International Biometric Society.
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