JSM 2005 Online Program

Abstract #303378

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Activity Number:	330
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistical Computing
Abstract - #303378
Title:	Accelerated Simulation with Finitized Power Series Distributions
Author(s):	Martin Levy*+ and Saeed Golnabi and James J. Cochran
Companies:	University of Cincinnati and and Louisiana Tech University
Address:	, Cincinnati, OH, 45221, United States
Keywords:	Poisson distribution ; Monte Carlo ; Computing
Abstract:	Finitization is a method that converts infinitely supported discrete power series distributions into other distributions having support of specified finite size, say n, while preserving the first n moments. Well-known power series distributions include Poisson, negative binomial, and logarithmic. As an approximation to the results of conventional simulations for Power Series distributions, finitization permits the use of aliasing, one of the most efficient and fastest ways to simulate finitely supported distributions. Here, we focus on the Poisson distribution. Because moments are preserved, "congestion measures" in simulated queues that depend on higher moments such as expected time in queue, or expected delay, remain faithful when using the finitized counterpart. The Kolgomorov-Smirnov statistic and its p-value measure the discrepancy between the distributions as a function of n. "Horse-race" statistics are presented to compare the speeds of simulating both conventionally from the parent distribution and the finitized version.

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