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How is AIC calculated for a regression model?
AIC is calculated for each model, and all other things being equal the model with the lower AIC (or SBC) should be chosen. Observation: Augmented versions of AIC and SBC, used in some texts, are as follows:
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.
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%.
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.
When to remove a non-significant variable from a regression?
I have run a multiple linear regression using stepwise regression to select the best model, however the best model returned has a non-significant variable. When I remove this the AIC value goes up indicating the model without the significant variable is a worse fit.
When to remove a non-significant predictor from a model?
When I remove this the AIC value goes up indicating the model without the significant variable is a worse fit. Should I remove the non-significant predictor or should I leave it in as it is a better model? Leave it in.
How does AIC provide a means for model selection?
Thus, AIC provides a means for model selection . AIC is founded on information theory. When a statistical model is used to represent the process that generated the data, the representation will almost never be exact; so some information will be lost by using the model to represent the process.