Using AIC vs Likelihood Ratio test for comparing Lognormal and Powerlaw distributions I am interested in comparing whether a lognormal or a power law are a better fit for a given set of data. Both distributions have been fit using MLE, with $x_{min}$ determined using KS-minimization a la Clauset et al. (2009).
I want to use the AIC to do the comparison of the two fits, but have seen warnings oon the AIC wikipedia article and here, not to use the AIC when one of the variables is log-transformed and the other isn't. In the PL we don't really have log transform to fit the variable, but for the LN we are, so is computing the AIC's and comparing them not OK to do? They are quite quite similar.
On the other hand some have pointed out here that the AIC is only for nested models, while the Wikipedia article on AIC cites Burham and Anderson (2002) stating the AIC is valid for non-nested models and in fact has the advantage over likelihood ratio tests of not requiring models to be nested.  The PL and LN are not nested models as I understand.
This is all in the context of using AIC instead of the likelihood-ratio (L-ratio) test suggested by Clauset et al. (2009), which Wikipedia is saying is invalid for non-nested distributions. The two test which seems to be a very similar test statistic (i.e. the difference in log-likelihoods, but the AIC introduces a parameter penalty) but assuming a different distribution for the significance of the difference. Why the difference in the test statistic's distribution?
As an additional question motivated by using the AIC vs L-ratio for comparing these two distributions, which one is correct for non-nested distributions and which is the better way to go in this case?

AIC IS NOT ONLY FOR NESTED MODELS. This is one of those fake news (myths) about AIC.
See: AIC Myths and Misunderstandings
There are several information criteria that can be used to compare different non nested models (BIC, DIC, ...).
You need to compare the models for the same data set. If you need to transform it for one of them, use a proper change of variable first, and then apply the model. For instance, for positive data, instead of using a normal on $\log(x_i)$, use a lognormal on $x_i$. This will take into account the always neglected Jacobian. This is: all models to be compared should be on the same scale and support.
If one model is not suitable for the data in the raw scale, it means that model is not good for the data, period.
