Contents
Can a model be selected based on the AIC?
I applied model selection (backward, or forward) based on the AIC on my data. And some of the selected variables ended up with a p-values > 0.05. I know that people are saying we should select models based on the AIC instead of the p-value, so seems that the AIC and the p-value are two difference concepts.
Which is a better value for the AIC function?
The AIC function is 2K – 2 (log-likelihood). Lower AIC values indicate a better-fit model, and a model with a delta-AIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to. Is this article helpful?
Which is the correct AIC for backward selection?
For backward selection using the AIC, suppose we have 3 variables (var1, var2, var3) and the AIC of this model is AIC*. If excluding any one of these three variables would not end up with a AIC which is significantly lower than AIC* (in terms of ch-square distribution with df=1), then we would say these three variables are the final results.
When to use AIC in an experimental design?
Your experimental design – for example, if you have split two treatments up among test subjects, then there is probably no reason to test for an interaction between the two treatments. Once you’ve created several possible models, you can use AIC to compare them. Lower AIC scores are better, and AIC penalizes models that use more parameters.
How does the derivation of Bic relate to AIC?
Importantly, the derivation of BIC under the Bayesian probability framework means that if a selection of candidate models includes a true model for the dataset, then the probability that BIC will select the true model increases with the size of the training dataset. This cannot be said for the AIC score.
Which is the best AIC for single variable at a time?
If you must use one, try LASSO or LAR. In fact using AIC for single-variable-at-a-time stepwise selection is (at least asymptotically) equivalent to stepwise selection using a cut-off for p-values of about 15.7%.