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
