Organ transplantation policy evaluation
WSC '95 Proceedings of the 27th conference on Winter simulation
Fitting multiple change-point models to data
Computational Statistics & Data Analysis
An Automated Multiresolution Procedure for Modeling Complex Arrival Processes
INFORMS Journal on Computing
Smooth flexible models of nonhomogeneous poisson processes using one or more process realizations
Proceedings of the 40th Conference on Winter Simulation
Statistical analysis of non-stationary series of events in a data base system
IBM Journal of Research and Development
Introduction to modeling and generating probabilistic input processes for simulation
Winter Simulation Conference
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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 follow the same statistical distribution, and if not, where the exact change points are, so that observations within two consecutive change points follow the same distribution. We propose two different methods based on likelihood ratio test and cluster analysis 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 methods. A well-known example is also considered to show the application of our findings in real world cases.