Organ transplantation policy evaluation
WSC '95 Proceedings of the 27th conference on Winter simulation
Estimating and simulating Poisson processes with trends or asymmetric cyclic effects
Proceedings of the 29th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 38th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 40th Conference on Winter Simulation
Smooth flexible models of nonhomogeneous poisson processes using one or more process realizations
Proceedings of the 40th Conference on Winter Simulation
Introduction to modeling and generating probabilistic input processes for simulation
Winter Simulation Conference
Simulation-based models of emergency departments:: Operational, tactical, and strategic staffing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Introduction to simulation input modeling
Proceedings of the Winter Simulation Conference
Modeling clustered non-stationary Poisson processes for stochastic simulation inputs
Computers and Industrial Engineering
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To automate the multiresolution procedure of Kuhl et al. for modeling and simulating arrival processes that may exhibit a long-term trend, nested periodic phenomena (such as daily and weekly cycles), or both types of effects, we formulate a statistical-estimation method that involves the following steps at each resolution level corresponding to a basic cycle: (a) transforming the cumulative relative frequency of arrivals within the cycle (for example, the percentage of all arrivals as a function of the time of day within the daily cycle) to obtain a statistical model with approximately normal, constant-variance responses; (b) fitting a specially formulated polynomial to the transformed responses; (c) performing a likelihood ratio test to determine the degree of the fitted polynomial; and (d) fitting to the original (untransformed) responses a polynomial of the same form as in (b) with the degree determined in (c). A comprehensive experimental performance evaluation involving 100 independent replications of eight selected test processes demonstrates the accuracy and flexibility of the automated multiresolution procedure.