Bayesian Model Averaging in the M-Open Framework

Clydec, Merlise

Iversen, Edwin S

Damien, P

Dellaportas, P

Polson, NG

Stephens, DA

2016-04-01T13:11:10Z

2013

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.

0191647004

9780191647000

https://hdl.handle.net/10161/11779

Oxford University Press

Bayesian Theory and Applications

10.1093/acprof:oso/9780199695607.003.0024

Model Selection

Model Uncertainty

Bayesian Model Averaging in the M-Open Framework

Book section

484

498

Duke

Statistical Science

Trinity College of Arts & Sciences

Published