Chapter

Bayesian model averaging in the M-open framework

Merlise Clydec and Edwin S Iversen

in Bayesian Theory and Applications

Published in print January 2013 | ISBN: 9780199695607
Published online May 2013 | e-ISBN: 9780191744167 | DOI: https://dx.doi.org/10.1093/acprof:oso/9780199695607.003.0024
Bayesian model averaging in the M-open framework

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This chapter presents a model averaging approach in the M-open setting using sample re-use methods to approximate the predictive distribution of future observations. It first reviews the standard M-closed Bayesian Model Averaging approach and decision-theoretic methods for producing inferences and decisions. It then reviews model selection from the M-complete and M-open perspectives, before formulating a Bayesian solution to model averaging in the M-open perspective. It constructs optimal weights for MOMA:M-open Model Averaging using a decision-theoretic framework, where models are treated as part of the ‘action space’ rather than unknown states of nature. Using ‘incompatible’ retrospective and prospective models for data from a case-control study, the chapter demonstrates that MOMA gives better predictive accuracy than the proxy models. It concludes with open questions and future directions.

Keywords: multiple proxy models; M-open framework; Bayesian inference; model averaging

Chapter.  7358 words.  Illustrated.

Subjects: Probability and Statistics

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