Bayesian Model Averaging: A Tutorial

Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions tha...

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Bibliographic Details
Published in:Statistical science 1999-11, Vol.14 (4), p.382-401
Main Authors: Hoeting, Jennifer A., Madigan, David, Raftery, Adrian E., Volinsky, Chris T.
Format: Article
Language:English
Subjects:
Approximation
Bayesian networks
Body fat
Circumferences
Linear regression
Modeling
P values
Predictive modeling
Probabilities
Regression analysis
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Summary:Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples. In these examples, BMA provides improved out-of-sample predictive performance. We also provide a catalogue of currently available BMA software.
ISSN:0883-4237
DOI:10.1214/ss/1009212519