2
$\begingroup$

Suppose I have $m$ competing models. Suppose also that I could classify these models into $s$ sets. For example, I could classify models of migration behavior conditional on climatic conditions by the geographic scope (global vs. local) of the climate measures. Now suppose I fit all of these models and calculate the information criterion. I understand how to find the "best" model. I understand how to compare that model to others and calculate either a relative likelihood (for AIC) or posterior odds (for BIC) based on the IC difference between two models. What I want to do, however, is compare the fit of models in set $s_i$ to models in set $s_{j\neq i}$. How do I do that? What if I want to compare the ICs across all sets (as opposed to two-set comparisons)? Do I need to do some statistical inference here, or, as with the two-model comparisons, can I take the ICs as given?

$\endgroup$
1
$\begingroup$

Using information criterion like AIC and BIC can be applied to compare non-nested models (as well as nested models) and so you should be able to simply compare the AIC's (or BIC's) of the models in set $s_i$ directly against those in set $s_{j:j\neq i}$.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.