On modeling nonhomogeneous Poisson process for stochastic simulation input analysis
Abstract
A validated simulation model primarily requires performing an appropriate input analysis mainly by determining the behavior of real-world processes using probability distributions. In many practical cases, probability distributions of the random inputs vary over time in such a way that the functional forms of the distributions and/or their parameters depend on time. This paper answers the question whether a sequence of observations from a process follows the same statistical distribution, and if not, where the exact change points are. We propose a Likelihood Ratio Test (LRT) based method to detect multiple change points when observations follow non-stationary Poisson process with diverse occurrence rates over time. Results from a comprehensive Monte Carlo study indicate satisfactory performance for the proposed method.
Cite
@article{arxiv.1402.7112,
title = {On modeling nonhomogeneous Poisson process for stochastic simulation input analysis},
author = {Issac Shams and Saeede Ajorlou and Kai Yang},
journal= {arXiv preprint arXiv:1402.7112},
year = {2014}
}
Comments
This paper has been withdrawn by the author due to an error in table 1