Abstract

Model choice criteria are explained, discussed and compared, including Akaike’s information criterion AIC, the Bayesian information criterion BIC and the focused information criterion FIC. Importantly, the uncertainties involved with model selec- tion are addressed, with discussions of frequentist and Bayesian methods. Finally, model averaging schemes, which combine the strength of several candidate models, are presented.