Hi,
I'm not a statistician so sorry for possible trivial questions ...

I want to perform a GoF test on sample data against several distribution
(like Extreme Value, Phase Type, Pareto, ...).

Since I suspect a long-tailed behaviour on data I want to use
Anderson-Darling (AD) GoF test because it's well known it's more sensible to
tail data.

Looking at R packages the only AD test is the AD normality test ("ad.test")
in the "nortest" package. So I think this function is not for me since
long-tailed samples aren't normally distribuited (right?!)

I've found the Marsaglia article ("Evaluating the Anderson Darling
distribution") where it seems I can consider the ECDF (empirical CDF) and
the theoretical as a uniformly [0,1] distributed data and then perform the
test like I had to compare two uniform distribution. The problem is the
theoretical CDF (i.e. the parameters of theoretical distribution) has been
estimated from the data against which I want to make the test. I've read
somewhere it's not a good technique to compare the distribution with the
above way because the resulting AD test might be biased.

So, finally, I don't know how to proceed ...

Can anyone give me a help or any reference (please remember I'm not a
statistician so do not write too technically)??

Thanks a lot to everyone!!

-- Marco

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