I typically use BIC as my understanding is that it values parsimony more strongly than does AIC. However, I have decided to use a more comprehensive approach now and would like to use AIC as well. I know that Raftery (1995) presented nice guidelines for BIC differences: 0-2 is weak, 2-4 is positive evidence for one model being better, etc.
I looked in textbooks and they seem strange on AIC (it looks like a larger difference is weak and a smaller difference in AIC means one model is better). This goes against what I know I have been taught. My understanding is that you want lower AIC.
Does anyone know if Raftery's guidelines extend to AIC as well, or where I might cite some guidelines for "strength of evidence" for one model vs. another?
And yes, cutoffs are not great (I kind of find them irritating) but they are helpful when comparing different kinds of evidence.
Best Answer
AIC and BIC hold the same interpretation in terms of model comparison. That is, the larger difference in either AIC or BIC indicates stronger evidence for one model over the other (the lower the better). It's just the the AIC doesn't penalize the number of parameters as strongly as BIC. There is also a correction to the AIC (the AICc) that is used for smaller sample sizes. More information on the comparison of AIC/BIC can be found here.