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Bayes model averaging with selection of regressors

Brown, Philip J., Vannucci, Marina, Fearn, T. (2002) Bayes model averaging with selection of regressors. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64 (Part 3). pp. 519-536. ISSN 1369-7412. (doi:10.1111/1467-9868.00348) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:554)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
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When a number of distinct models contend for use in prediction, the choice of a single model can offer rather unstable predictions. In regression, stochastic search variable selection with Bayesian model averaging offers a cure for this robustness issue but at the expense of requiring very many predictors. Here we look at Bayes model averaging incorporating variable selection for prediction. This offers similar mean-square errors of prediction but with a vastly reduced predictor space. This can greatly aid the interpretation of the model. It also reduces the cost if measured variables have costs. The development here uses decision theory in the context of the multivariate general linear model. In passing, this reduced predictor space Bayes model averaging is contrasted with single-model approximations. A fast algorithm for updating regressions in the Markov chain Monte Carlo searches for posterior inference is developed, allowing many more variables than observations to be contemplated. We discuss the merits of absolute rather than proportionate shrinkage in regression, especially when there are more variables than observations. The methodology is illustrated on a set of spectroscopic data used for measuring the amounts of different sugars in an aqueous solution.

Item Type: Article
DOI/Identification number: 10.1111/1467-9868.00348
Uncontrolled keywords: Bayesian model averaging; decision theory; multivariate general linear model; QR-updating; ridge regression; variable selection
Subjects: H Social Sciences > HA Statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Judith Broom
Date Deposited: 19 Dec 2007 18:19 UTC
Last Modified: 16 Nov 2021 09:39 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Brown, Philip J..

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