A note on model uncertainty in linear regression - Candolo, C.; Davison, A. C.; Demetrio, CGB (Blackwell Publ Ltd, 2003)
We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.
Keywords: akaike information criterion; Bayes information criterion; bootstrap; model averaging; model uncertainty; prediction