Bayesian inference for double Pareto lognormal queues
arXiv:1011.3411 · doi:10.1214/10-AOAS336
Abstract
In this article we describe a method for carrying out Bayesian estimation for the double Pareto lognormal (dPlN) distribution which has been proposed as a model for heavy-tailed phenomena. We apply our approach to estimate the and queueing systems. These systems cannot be analyzed using standard techniques due to the fact that the dPlN distribution does not possess a Laplace transform in closed form. This difficulty is overcome using some recent approximations for the Laplace transform of the interarrival distribution for the system. Our procedure is illustrated with applications in internet traffic analysis and risk theory.
Published in at http://dx.doi.org/10.1214/10-AOAS336 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)