Brian, I assume you've got the Burnham and Anderson, 2002, book. Everything, and more, is in there.
If I recall correctly, AIC weights are calculated from deltaAIC, so I don't understand how you are having disagreements. Model weights quanitfy the deltaAIC so that parameters can be "model averaged," i.e. a weighted average of parameter estimates from the models. One philosophical advantage of model selection is trying to get away from black/white significant/non-sig, like the p<0.05 rule. By assessing the relative value of models, it forces us to deal with reality and varying levels of support for models, which is not black/white, instead of assuming it's true/not true depending on the p value. So if you are looking for a hard and fast rule of what model you should use and not, there isn't one, except model-average your parameters. Tyler Grant ----- Original Message ----- From: "Brian D. Campbell" <[EMAIL PROTECTED]> To: <[email protected]> Sent: Tuesday, October 31, 2006 9:52 AM Subject: Model selection using AIC > When comparing models using an information-theoretic approach, I have seen > several means to assess the likelihood of candidate models. One method > uses > the AIC value of a given model relative to best model in the set, i.e. > delta > AIC. When delta AIC is less than or equal to 2, the given model is > suggested to be within the range of plausible models to best fit the > observed data. However, one can also compute Akaike's weights, which > seems > to me a more intuitive means of assessing the likelihood of a candidate > model being the best for the observed data. Have guidelines on use of > Akaike's weights to assess model likelihood been published somewhere, for > example, when the evidence ratio (ith model relative to the best) is above > a > given value? I have found a comparison of these two approaches can yield > somewhat inconsistent results and would appreciate any feedback on what > others have found. > > Sincerely: > > Brian D. Campbell >
