AIC and other IT indexes criteria for for backward, forward and stepwise regression
This is a reasonable question, but it isn't at all specific to mixed models (which is the topic of this mailing list). You could try CrossValidated (https://stats.stackexchange.com). I'm sure opinions differ a lot, and answers will almost certainly depend on your goals and context, but *if* I were going to do model selection (which I think is very often a bad idea!) I would simply pick the model with the minimum AIC, which will (asymptotically) have the smallest expected Kullback-Leibler distance.
On 2019-12-18 6:07 a.m., Mario Garrido wrote:
Dear users, Im currently exploring on the use of AIC and other I-T indexes criteria for backward, forward and stepwise regression. Usually, when applying IT indexes for Multimodal Inference, we choose a set of 'good models' depending on different criteria, but mainly, all models with delta AIC<2, and then we averaged the estimates between the set of models or make conclusions based on the set of models, no need to average. However, if Im not wrong, the goal of backward etc is to get to one 'best' final model. I understand the use of AIC in this framework but, is there any criteria to select the best model in this case? Do I simply have to choose the model with the lowest AIC no matter whether there is another model whose delta is less than 2? Does it depend on a personal criteria? For example, if my 'maximal' or saturated model has the lowest AIC and the model dropping one variable has a delta of 0.5, which model to choose? I was looking on the web and I have found no answer to this. So, any literature recommendation or advice will be welcome. Thanks