David Kaplan
On the Quantification of Model Uncertainty: A Bayesian Perspective
University of Wisconsin–Madison
issues model selection dominated theoretical applied statistical literature decades model selection methods ridge regression lasso elastic net replaced ad hoc methods stepwise regression means model selection end however methods lead single final model often taken model considered ahead time thus ignoring uncertainty inherent search final model one method enjoyed long history theoretical developments substantive applications accounts directly uncertainty model selection bayesian model averaging bma bma addresses problem model selection selecting final model rather averaging space possible models could generated data purpose paper provide detailed uptodate review bma focus foundations bayesian decision theory bayesian predictive modeling consider selection parameter model priors well methods evaluating predictions based bma also consider important assumptions regarding bma extensions model averaging methods address assumptions particularly method bayesian stacking simple empirical examples provided directions future research relevant psychometrics discussed