Biomathematics and Statistics Scotland (BioSS)
Dia i hora:
Divendres 16 de juny de 2017 a les 12:00.
Aula A1 del CRM, Facultat de Ciències, campus de la UAB.
1 hora aproximadament
L'assistència a aquest seminari és gratuïta.
Per motius d'aforament us agrairem que us enregistreu en el següent formulari: enllaç
Model selection is difficult, even in the apparently straightforward case of choosing between linear regression models. There has been a lively debate in the statistical ecology literature in recent years, where some authors have sought to evangelise AIC in this context while others have disagreed strongly.
A series of discussion articles in the journal Ecology in 2014 dealt with part of the issue: the distinction between AIC and p-values. But within the family of information criteria, is AIC always the best choice?
Theory suggests that AIC is optimal in terms of prediction, in the sense that it will minimise out-of-sample root mean square error of prediction. Earlier simulation studies have largely borne out this theory. However, we argue that since these studies have almost always ignored between-sample heterogeneity, the benefits of using AIC have been overstated.
Via a novel simulation framework, we show that relative predictive performance of model selection by different information criteria is heavily dependent on the degree of unobserved heterogeneity between data sets.