Artículos de revistas
A note on model uncertainty in linear regression
Fecha
2003-01-01Registro en:
Journal Of The Royal Statistical Society Series D-the Statistician. Oxford: Blackwell Publ Ltd, v. 52, p. 165-177, 2003.
0039-0526
10.1111/1467-9884.00349
WOS:000183546800003
Autor
Swiss Fed Inst Technol
Universidade Federal de São Carlos (UFSCar)
Universidade Estadual Paulista (Unesp)
Institución
Resumen
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.