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AIC should rarely be used, as it is really only valid asymptotically. It is almost always better to use AICc (AIC with a correction for finite sample size). AIC tends to overparameterize: that problem is greatly lessened with AICc. The main exception to using AICc is when the underlying distributions are heavily leptokurtic.
Aug 30, 2016 · AIC tries to select a model (among the examined ones) that most adequately describes reality ...
Mar 4, 2013 · AIC is telling you how good your model fits for a specific mis-classification cost. AUC is telling you how good your model would work, on average, across all mis-classification costs. When you calculate the AIC you treat your logistic giving a prediction of say 0.9 to be a prediction of 1 (i.e. more likely 1 than 0), however it need not be.
$\begingroup$ See the definition of AIC: $-2\log\mathcal{L}(\hat\theta)+2p$ where the vector of parameters, $\theta$ are evaluated at the maximum (i.e. all the elements of $\hat\theta$ are MLEs); e.g. see Wikipedia Akaike information criterion: Definition.
AIC and BIC hold the same interpretation in terms of model comparison. That is, the larger difference in either AIC or BIC indicates stronger evidence for one model over the other (the lower the better). It's just the the AIC doesn't penalize the number of parameters as strongly as BIC.
Usually, AIC is positive; however, it can be shifted by any additive constant, and some shifts can result in negative values of AIC. It is not the absolute size of the AIC value, it is the relative values over the set of models considered, and particularly the differences between AIC values, that are important.
Jan 6, 2017 · The AIC is somewhat of an exception to this, because its correction for the amount of parameters makes unnested models made for the same outcome on the same data, more comparable. So to conclude, no, there is no easy way of comparing the specific AICs using a statistical test.
Lower AIC values are still better, both in the Wikipedia article and in the video. In the middle of the video, the presenter walks through reading the output and shows that dropping C2004 would lead to a new model with AIC = 16.269. This is the lowest AIC possible, so it is the best model, so the variable you should drop is C2004.
As a quick rule of thumb, selecting your model with the AIC criteria is better than looking at p-values. One reason one might not select the model with the lowest AIC is when your variable to datapoint ratio is large. Note that model selection and prediction accuracy are somewhat distinct problems.
The difference in AIC (or BIC) for two models is twice the log-likelihood ratio minus a constant: it follows immediately that in any particular case selecting the AIC corresponds to performing a likelihood-ratio test, but that in different cases it corresponds to tests of different significance levels.
