The Model Averaging Option

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Fitting Linear Models • Stepwise Regression Models • The Model Averaging Option

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The Model Averaging Option

The model averaging technique enables you to average the fits for a number of models, instead of picking a single best model. The result is a model with excellent prediction capability. This feature is particularly useful for new and unfamiliar models that you do not want to overfit. When many terms are selected into a model, the fit tends to inflate the estimates. Model averaging tends to shrink the estimates on the weaker terms, yielding better predictions. The models are averaged with respect to the AICc weight, calculated as follows:

AICcBest is the smallest AICc value among the fitted models. The AICc Weights are then sorted in decreasing order. The AICc weights cumulating to less than one minus the cutoff of the total AICc weight are set to zero, allowing the very weak terms to have true zero coefficients instead of extremely small coefficient estimates.

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Help created on 7/12/2018