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arXiv · 1402.7112

On modeling nonhomogeneous Poisson process for stochastic simulation input analysis

Issac Shams·Saeede Ajorlou·Kai Yang
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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.

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Issac Shams, Saeede Ajorlou, Kai Yang. 2014-02-28. On modeling nonhomogeneous Poisson process for stochastic simulation input analysis. https://arxiv.org/abs/1402.7112

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