Comparison of the tails of two sample distributions I have two set of data that are roughly centered around zero but I suspect that they have different tails. 
I know a few tests to compare the distribution to a normal distribution, but I would like to compare directly the two distributions.
Is there a simple test to compare the fatness of tail of 2 distributions?
Thanks
fRed
 A: This question seems to belong to the same family as this earlier one about testing whether two samples have the same skew, so you may like to read my answer to that. I believe that L-moments would be useful here too for the same reasons (specifically L-skewnesskurtosis in this case).
A: The Chi Square test (Goodness-of-Fit test) will be very good at comparing the tails of two distributions since it is structured to compare two distributions by buckets of values (graphically represented by a histogram).  And, the tails will consist in the far most buckets.
Even though this test focuses on the whole distribution, not just the tail you can readily observe how much of the Chi Square value or divergence is derived by the difference in the tails's fatness.
Watch that the derived histogram may actually give you visually much more information regarding the respective fatness of the tails than any test related statistical significance.  It is one thing to state that tails fatness are statistically different.  It is another to visually observe it.  They say a picture is worth a thousand words.  Sometimes it is also worth a thousand numbers (it makes sense given that graphs encapsulate all the numbers).      
A: Constructing a threshold, saying lambda, we can test equality of two means or variances of the two
distributions restricted on the tail region (\lambda, infinity) based on two data sets
of observations falling in this tail region. Of course, the two sample t-test or F-test
may be OK but not be poweful since random variable restricted on this tail region is not normal even the original ones are.
A: How about fitting the generalized lambda distribution and bootstrapping confidence intervals on the 3rd and 4th parameters?
