Thanks!! It does look like the easiest thing is direct ML; the code for a normal mixture is in the book, so all I have to do is modify that for a sum of a hyper-exponential, for which I have an approximate mean and CV, and a normal, for which I have an approximate mean and SD.
I have two big peaks, one near zero which is probably hyperexponential with a CV about 3, and the other near 600 seconds (a refresh that happens every ten minutes) which looks Gaussian with a very small standard deviation. I think what I'm going to do is fit the two peaks using ML, since I know where they are, then subtract them out and look at the structure of the residuals. The stuff over 600 seconds is sparse and totally uninteresting. After I'm done with this, I get to look at the distribution of the network traffic. The good news is that I get those inter-arrival times to the nearest microsecond. :) -- M. Edward (Ed) Borasky mailto:[EMAIL PROTECTED] http://www.borasky-research.net "Suppose that tonight, while you sleep, a miracle happens - you wake up tomorrow with what you have longed for! How will you discover that a miracle happened? How will your loved ones? What will be different? What will you notice? What do you need to explode into tomorrow with grace, power, love, passion and confidence?" -- L. Michael Hall, PhD > -----Original Message----- [snip] > For all of these see MASS (the book) and its on-line complements. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help