Statistics Seminars: Selection of weights for weighted model averaging
13 March 2007 10:00 in E101
Suppose a quantity is to be predicted and various models could be used. The approach in model selection is to use just the prediction of the model that appears to be best. An alternative is to form a weighted average of the predictions given by the different models. But what weights should be given to the different models? Should the weight given to a model be reduced if it is very similar to another model? What if two models are virtually identical - should they each be given half the weight that they would otherwise receive?
This talk considers methods of assigning weights on the basis of the correlation structure between models. (In the case of Bayesian model averaging, the focus is on assigning the prior weights.) Different weighting strategies are proposed and desirable properties in a weighting scheme are suggested. Simulation is used to compare the weighting schemes in situations where optimal weights can be determined.
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