suppose I have a data set $x_1, \ldots, x_n$ and I would fit a normal, an exponential and a uniform distribution to them. The fitting function spits out a bunch of goodness-of-fit statistics, e.g. the AIC, BIC, chi-square, Kolmogorov-Smirnov, etc.
I am trying to convince someone that the AIC is not appropriate here, because we have different log-likelihoods, and sometimes different number of parameters, depending on the distributions. I would prefer the p-value of the Kolmogorov-Smirnov-Test to compare the fits.
Is my approach justified? How can I convince my coworker the AIC is not okay here (he likes to see a cited paper or something equivalent)?
Thanks in advance!
edit: Specifically, I was shown this article: http://www.vosesoftware.com/whitepapers/Fitting%20distributions%20to%20data.pdf
I have no idea what to say to this. Page 4 lists the flaws of the chi-squared, Kolmogorov-Smirnov etc, and page 5 and 6 praise the AIC. Is he right?