I have a collection of data which includes inter-arrival times of requests to a server. What I've done so far with it is use "sm.density" to explore the distribution, which found two large peaks. However, the peaks are made up of Gaussians, and that's not really correct, because the inter-arrival time can never be less than zero. In fact, the leftmost peak is centered at somewhere around ten seconds, and quite a bit of it extends into negative territory.
What I'd like to do is fit this dataset to a mixture (sum) of exponentials, hyper-exponentials and hypo-exponentials. My preference is to use the well-known branching Erlang approximation (exponential stages) to the hyper- and hypo-exponentials. In this approximation, a distribution is specified by its mean and coefficient of variation. So far, what I've been able to come up with in a literature search has been something called the Expectation Maximization algorithm. And I haven't been able to locate R code for this. So my questions are: 1. Is EM the "right way" to go about this, or is there something better? 2. Is there some EM code in R that I could experiment with, or do I need to write my own? 3. Is there a way this could be done using the existing R kernel density estimators and some kind of kernel that is zero for negative values of its argument? -- 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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
