Robust Bayesian model selection

Robust Bayesian model selection

This paper extends the robust Bayesian inference in misspecified models of Müller (2013, Econometrica) to Bayesian model selection of a set of misspecified models. It is shown that when a model is misspecified, under the Kullback-Leibler loss function, the risk associated with Müller's posterio...

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Bibliographic Details
Main Authors: LI, Yong, YU, Jun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
Subjects:
Model selection
Model misspecification
Artificial posterior distribution
Sandwich-covariance matrix; Markov chain Monte Carlo
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/2112
https://ink.library.smu.edu.sg/context/soe_research/article/3112/viewcontent/Robust_Bayesian_model_selection_2013.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:This paper extends the robust Bayesian inference in misspecified models of Müller (2013, Econometrica) to Bayesian model selection of a set of misspecified models. It is shown that when a model is misspecified, under the Kullback-Leibler loss function, the risk associated with Müller's posterior is less (weakly) than that with the original posterior distribution asymptotically. Based on this new result, two new information criteria are proposed for model selection under model misspecification. Sufficient conditions are provided for the risk associated with Müller's posterior to be strictly smaller.

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