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Activity Number:
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298
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302172 |
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Title:
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Model Selection in Canonical Variate Analysis Using Bayesian Model Averaging
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Author(s):
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Robert Noble*+ and Eric P. Smith and Keying Ye
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Companies:
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Miami University and Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University
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Address:
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Department of Mathematics & Statistics, Oxford, OH, 45056,
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Keywords:
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Bayesian model averaging ; model selection ; canonical variate analysis
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Abstract:
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The standard methodology when building discriminant analysis models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for datasets with a large number of variables, a stepwise procedure is usually implemented. The stepwise procedure of model selection was designed for its computational efficiency and is not guaranteed to find the "best" model with respect to any optimality criteria. Many times there will be several models that exist that may be competitors of the best model in terms of the selection criterion, but classical model building dictates that a single model be chosen to the exclusion of all others. An alternative to this is to use Bayesian model averaging (BMA), which uses the information from all models based on how well each is supported by the data. The BMA model is weighted average of all possible models where the individual variable configuration.
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